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Quantitative Developer, Research & ML Engineering, Systematic Macro Millennium is a top tier global hedge fund with a strong commitment to leveraging market innovations in technology and data to deliver high-quality returns. Job Description A collaborative and entrepreneurial systematic macro pod is seeking an experienced Quantitative Developer with a machine learning focus. You will develop and deploy machine learning models on high-frequency market data, and build the research and compute infrastructure behind them. Location London Principal Responsibilities Design, train and productionize large-scale machine learning models across both classical and deep learning approaches, applied to high-frequency data Enhance and optimize the pod's end-to-end machine learning pipeline, from large-scale data processing and distributed computation to scalable parameter search and validation Contribute to improving the speed, scalability, and reliability of the pod's wider signal development environment, ensuring consistent and efficient migration from research to production Partner with broader technology teams to make effective use of shared internal platforms and Services Qualifications Master's or PhD/Post doctorate in Computer Science, Mathematics, Statistics, Engineering, Physics, or a related quantitative discipline, from a leading institution Preferred Technical Skills 3+ years of professional experience in software engineering, quantitative development, or a related computational role Experience developing and validating machine learning models on large, complex datasets, across both classical and deep learning approaches, in industry or academia Experience building distributed computing systems for machine learning applications Strong Python programming skills beyond the standard research stack - parallelism, distributed compute, and native acceleration such as Python or C++ bindings Familiarity with C++ is a strong plus, alongside the software engineering fundamentals to pick it up quickly Experience building data-intensive tools, research workflows, or model development infrastructure Strong Linux development experience Experience building agentic AI systems - tool use, orchestration, and evaluation High Valued Experience Experience with backtesting and awareness of common research pitfalls such as overfitting, lookahead bias, and survivorship bias Understanding of systematic trading strategies and quantitative research workflows Knowledge of market microstructure Experience supporting production research workflows or model deployment in a front-office environment

The Applied Innovation (of AI) Engineering team is an elite machine learning group strategically located within the Chief Data Analytics Office (CDAO) Technology group of JP Morgan Chase. AI Engineering tackle business critical priorities using innovative machine learning techniques and technologies with a focus on machine learning for Software, Cybersecurity and Technology Infrastructure. The team partners closely with all lines of business and engineering teams across the firm to execute long-term projects in these areas that require significant machine learning development to support JPMC businesses as they grow. As a Principal Software Engineer at JPMorganChase within CDAO Technology Group in the Applied Innovation (of AI) Engineering team, you will design, develop, deploy, and maintain advanced AI products. You'll collaborate with software engineers, data engineers, and data scientists to deliver trusted solutions. Your role is central to executing long-term projects that drive innovation and support our business growth. You'll contribute to a culture of inclusion and technical excellence. Job Responsibilities Collaborates with engineers, data scientists, and product owners, to deliver products to production. Builds and maintain data pipelines for analytics, model evaluation, and training (includes versioning, compliance and validation). Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Provides feedback and proposes improvements to architecture governance practices Creates secure and high-quality production code, and reviews and debugs code written by others. Maintains algorithms that run synchronously with appropriate systems Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture Regularly provides technical guidance and direction to support the business and its technical teams and vendors Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale Required Qualifications, Capabilities and Skills Proven programming/scripting skills with multiple modern programming languages including Python, Golang, TypeScript and similar technologies in an AWS /Cloud environment Experience in the use of GPT, LLM, RAG, Gen AI, Prompt Engineering related technologies Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages Current experience of agile methodologies such as CI/CD, Applicant Resiliency, and Security Solid understanding of software applications and technical processes within a related technical discipline (e.g. Synchronous and non-synchronous APIs, Concurrency, etc.) Creates complex and scalable coding solutions using appropriate software design frameworks ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. ABOUT THE TEAM Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.

Orbital uses AI to build data center hardware that outperforms the competition. Our AI simulates materials at the atomic level, tests millions of hardware configurations in the time traditional methods test hundreds and finds optimal designs - not just good ones. The result is shipped hardware with specs incumbents can't match: 1 MW/rack, PUE Every deployment generates field data that improves our AI models. Better models produce better hardware. Better hardware enables more capable AI. The loop is already closed and tightening with each build cycle. We are not just beneficiaries of AI progress, we are a lever on its rate. Data Centers are where we start, because the market is urgent and the specs are demanding. But the AI-accelerated development process we've built - materials discovery, hardware design, manufacturing optimization - applies to any complex physical system. Data Centers are the proof point, not the ceiling. We have sites in London, Canada and the USA, building teams across ML research, Product development, Mechanical engineering, and Chemical engineering. If you want to work where AI meets atoms, we'd like to hear from you. As a Staff Machine Learning Engineer at Orbital, you will architect cutting-edge AI systems for the multi-scale design of physical technologies. When we say multi-scale, we mean it: we build world-class foundation models for simulating both the microscopic motion of atoms and the macroscopic flow of liquids in 1GW data centers. We then co-design across these different scales using the ingenuity of our scientists and engineers, augmented with best-in-class domain agents. In this role you will set exceptionally high technical standards and drive projects from prototype through to production deployment. First and foremost, we want to work with someone with a love of craftsmanship, continual learning, and building systems that scale. We also value low ego, and a genuine passion for using AI to solve major global industrial technology challenges. Key Responsibilities Set the technical bar and ensure engineering excellence Establish and maintain exceptionally high standards for code quality, system architecture and ML research and engineering practices through hands on coding and technical review Design robust, well engineered systems that others can build upon, balancing research velocity with production requirements Drive technical decisions on model selection, training approaches and deployment strategies Deliver high impact AI projects across diverse domains Develop and deploy AI solutions across the entire technology development pipeline- computational chemistry simulations, agentic workflows and beyond Rapidly upskill in new technical areas through close collaboration with domain experts (no prior chemistry or materials experience required) Demonstrate strong implementation skills through hands on development, contributing significantly to the codebase Balance research rigour with pragmatic engineering to deliver production ready systems at scale Push the frontier of ML research Design and implement novel ML architectures for complex scientific domains, with work that meets publication standards at top tier conferences Drive research projects from conception through to deployment, showing initiative and technical depth Engage continuously with the latest ML literature, staying current with developments in foundation models, generative AI and scientific machine learning What We're Looking For ONE of: 5+ years of professional experience in ML/AI research or engineering. A relevant PhD + 2 years of professional experience in ML/AI research or engineering. Proven experience training, evaluating and productionising AI models at scale, with deep understanding of the full ML lifecycle from research to deployment Strong engineering fundamentals with the ability to write high-quality, maintainable code and architect robust systems A strong ability to reason about algorithms, system design, linear algebra, probabilistic concepts and ML engineering trade offs An ability to debug complex machine learning systems through meticulous attention to detail, testing of edge cases and carefully selected ablations A genuine interest in building AI systems that enable breakthrough scientific and industrial applications Upon reading Hamming's You and Your Research, you resonate with quotes such as: "Yes, I would like to do first class work" "You should do your job in such a fashion that others can build on top of it, so they will indeed say, 'Yes, I've stood on so and so's shoulders and I saw further.'" "Instead of attacking isolated problems, I made the resolution that I would never again solve an isolated problem except as characteristic of a class" Bonus Experience with physics informed or chemistry focused AI applications. Experience building or fine tuning large language models. Experience with agent based systems, tool use or agentic workflows. Contributions to open source ML projects or published research. Orbital is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Moniepoint is a global fintech building modern financial services for millions of people and businesses across high-growth markets. We provide payments, banking, credit, and financial management tools - reliable products that people and businesses use every day to run their lives, grow their companies, and move money safely. Our mission is simple: to enable financial happiness for every African, everywhere. And this is day one. We've grown rapidly in Nigeria and the UK, and we're now expanding our product, engineering, and analytics teams. Our work ranges from building financial infrastructure to designing intuitive customer experiences for emerging markets - solving real, meaningful problems at scale. We onboard over a million new customers each month, process 100s of billions of dollars in payments annually, and support tens of millions of users across our ecosystem. Our teams operate in a fast-paced, high-impact environment alongside international leaders from companies like Glovo, Bolt, Monzo, Klarna, and Tide. If you want to build our data science function from the ground up, work with one of Africa's highest-scale fintech data sets, and ship products that influence tens of millions of users, this is an exceptional time to join. About the role We're looking for a hands-on Spain-based Data Science Manager to lead our consumer credit data science efforts in a high-growth environment. This role is pivotal in building and scaling a team responsible for pricing, credit limit modelling, and production credit model deployment, with direct ownership of decisions that impact millions of customers. You'll work closely with our Consumer Credit, Product, and Engineering teams to shape how we assess and price risk, design credit products, and measure outcomes across the credit lifecycle. Sitting at the intersection of data science, credit risk, and product, you'll build the analytics and modelling foundations that inform underwriting, customer acquisition, retention, and portfolio performance at scale. Developing credit scoring, affordability, and behavioural models to support underwriting, pricing, and collections Design and run experiments to optimise approval rates, loss rates, and profitability Partner with product squads to embed decision logic into real-time systems Ensure data quality, compliance, and ethical use of models across all decisioning processes Mentor product squads on best practices in experimentation and data-driven decision making Provide models to optimise outcomes in collections, churn management and user retention We would love to hear from you if You're comfortable with Statistics - and have a Degree or qualifications in a quantitative field (Statistics, Mathematics, Engineering or similar) You have +5 years of experience in data science, decision science, or risk analytics within financial services, including +2 years in managerment Working knowledge of credit risk, consumer lending, and regulatory considerations Proficiency in SQL and at least one modelling/programming language (Python, R) Experience with A/B testing, machine learning, collections modelling and churn management Ability to translate complex analyses into clear recommendations for business stakeholders High ownership mindset and comfort working in fast-paced, cross-functional teams What Moniepoint Can Offer You The opportunity to drive financial inclusion and shape the future of the African financial ecosystem The chance to work on innovative and impactful projects A dynamic, diverse, and collaborative environment where every team member's voice is recognised and valued Continuous learning and career growth opportunities Competitive salary, individual performance bonuses, and firmwide performance bonus The company covered health insurance plans Pension plans What to expect in the hiring process Introductory call with one of our recruiters Initial interview with the Director of Data Science Take-home task (hands-on coding or marketing modelling case study) Business case interview with our Head of Marketing Strategy & Data Live technical coding interview with the Director of Data Science Culture & values interview (60 minutes) with the Director of Data Science Moniepoint is an equal-opportunity employer. We believe diversity makes us stronger and are committed to creating an inclusive environment for all employees and candidates.

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm's data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm's commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly. As a Senior Manager - SRE at JPMorgan Chase within the AIML Data Platforms and Chief Data and Analytics Team, you will lead a team of 3-7 SRE engineers and scale the AI/ML Data Platform, deliver advanced technology products focused on data and analytics. You will tackle complex cloud data platform challenges, especially around Data Lake Tools. In this role you will work in an agile environment, collaborating with cross-functional teams. Job Responsibilities: Leads a team of Site Reliability Engineers and support critical application 24x7 Implements Site Reliability Engineering (SRE) best practices to ensure reliability, scalability, and performance of data platforms. Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems. Scale and maintain a managed AWS Databricks platform, and provides engineering and operational support for the platform to Application/Engineering teams. Drives reuse-first adoption of enterprise-authorized AI capabilities within the work environment to improve reliability operations and customer experience outcomes, with human-in-the-loop validation and appropriate handling of sensitive data. Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture. Drives continuous improvement in system observability, alerting, and capacity planning. Collaborates with engineering and data teams to optimize infrastructure and deployment processes, focusing on automation and operational excellence. Performs platform design, set-up and configuration, workspace administration, resource monitoring, providing engineering support to Data Engineering teams, Data Science/ML, and Application/Integration teams. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems. Develops secure high-quality production code, and reviews and debugs code written by others. Adds to team culture of diversity, opportunity, and respect. Develops and maintains incident response procedures, including root cause analysis and postmortem documentation. Required Qualifications, Capabilities, and Skills: Experience with formal training or certification on software engineering concepts. Strong understanding of SRE principles, including SLIs, SLOs, error budgets, and incident management. Ability to manage highly technical team of Site Reliability Engineers. Experience leading teams in the safe use of enterprise-authorized AI capabilities within the work environment for reliability engineering workflows, including validation habits and awareness of data sensitivity. Ability to set and reinforce organization-level practices for reviewing AI-assisted recommendations and escalating uncertain decisions while maintaining resiliency, security, and auditability outcomes. Extensive experience with AWS, Databricks, or Snowflake platform administration and engineering support is a MUST. Experience with monitoring tools, automation frameworks, and CI/CD pipelines. Proficient in Python application program development with use of automated unit testing. Experience with Terraform development and understanding of Terraform enterprise. Experience in delivering system design, application development, testing, and operational stability. Knowledge of Big Data distributed compute frameworks like Spark, Glue, MapReduce etc. Excellent troubleshooting, analytical, and communication skills. Preferred Qualifications, Capabilities, and Skills: Multi Region disaster recovery setup, monitoring and testing. Experience in Data pipelines using Spark. Exposure to AWS & Databricks Platform administration. Knowledge of containerization (Docker, Kubernetes) and orchestration. Familiarity with distributed systems and large-scale data processing. ABOUT US J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world's most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation. ABOUT THE TEAM Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.

About Anthropic Anthropic's mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the Role As a Regional Research Economist at Anthropic, you will collaborate with governments, academia, industry, and civil society in your region to measure and understand AI's effects on the economy and explore research driven policy interventions. You will contribute to the development of the Anthropic Economic Index and its extension to generate regionally relevant insights, establish new methodologies to measure the usage, diffusion, and impact of AI throughout the economy, and broaden access to and usage of the insights generated by the Index. You will use frontier methods in econometrics, machine learning, and structural estimation, driving impact that shapes both policy discussions externally and informs Anthropic's internal business and product decisions. Our team combines rigorous empirical methods with novel measurement approaches. We're building first of its kind datasets tracking AI's impact on labor markets, productivity, and economic transformation, using our privacy preserving measurement system to analyze millions of real world AI interactions. Key responsibilities Build and maintain relationships with academic institutions, policy think tanks, and other research partners as the primary point of contact for economic impact work in your region. Advance research collaborations that answer country or regional specific economic impact questions. Translate research insights into actionable recommendations for policy discussions. Make fundamental contributions to the development and expansion of the Anthropic Economic Index, including country/regional specific analysis. Design and collaborate on empirical research on AI's economic effects with governments, academia, industry, and civil society in your region. Develop new methodological approaches, in collaboration with partners in your region, for studying AI's impact on: Labor markets and the future of work Productivity and task transformation Economic inequality and displacement Industry specific disruption and adaptation Aggregate economic trajectories (GDP, productivity, unemployment) under varying AI adoption scenarios Work cross functionally with other technical teams to improve our measurement infrastructure and data collection. Amplify external engagement through research publications, policy briefs, and presentations to diverse stakeholders. Minimum qualifications PhD in Economics. Strong track record of empirical research, particularly studies combining novel data sources and economic theory or those implementing frontier methods in causal inference and machine learning. Demonstrated experience working in and relevant relationships across the region. Experience relevant to the study of AI's impact on the economy, including: Labor market analysis and occupational change Task based approaches to technological transformation Large scale data analysis and econometric methods Large language models for social science research Policy relevant economic research Experimental and quasi experimental methods for causal inference Macroeconomic modeling and time series forecasting Agent based modeling or large scale simulation Technical skills including: Proficiency in Python, R, SQL, or similar tools for large scale data analysis. Experience working with novel datasets and measurement systems. Comfort learning new technical tools and frameworks. Demonstrated ability to: Lead complex research projects from conception to publication. Communicate technical findings to diverse audiences. Build relationships across academic, policy, and industry communities. Strong interest in ensuring AI development benefits humanity. Comfort working with AI systems and ability to think critically about their capabilities and limitations. Representative projects How Australia Uses Claude: Findings from the Anthropic Economic Index Anthropic Economic Index Report: Economic Primitives Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption Estimating AI productivity gains from Claude conversations The Anthropic Economic Index Additional Information For this role, we're looking for candidates who can combine rigorous economic analysis with novel measurement approaches to understand AI's transformative effects on the economy. The ideal candidate will be comfortable working at the intersection of empirical economics, technological change, and policy impact. Annual Salary £180,000 - £190,000 GBP Logistics Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience. Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience. Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position. Location based hybrid policy: We expect all staff to be in one of our offices at least 25% of the time. Some roles may require more office time. Visa sponsorship: We sponsor visas but cannot always meet every candidate's requirements.

Design Engineer (Machine Tools/CNC) £50,000 to £55,000 + 36 Days Holiday + Overtime + Life Insurance + Paid Medical Appointments + Employee Discount Scheme + Employee Matched Pension Leeds, Bradford, Keighley Are you a Design Engineer or similar, looking to join a role where you can be trained on the company specific CAD package, whilst being offered a generous holiday scheme, paired with a range of click apply for full job details

You are on our Global Page. To view content relevant to your location, select a different country or region. Data Science Manager (Credit) Who we are Moniepoint is a global fintech building modern financial services for millions of people and businesses across high-growth markets. We provide payments, banking, credit, and financial management tools - reliable products that people and businesses use every day to run their lives, grow their companies, and move money safely. Our mission is simple: to enable financial happiness for every African, everywhere. And this is day one. We've grown rapidly in Nigeria and the UK, and we're now expanding our product, engineering, and analytics teams. Our work ranges from building financial infrastructure to designing intuitive customer experiences for emerging markets - solving real, meaningful problems at scale. We onboard over a million new customers each month, process 100s of billions of dollars in payments annually, and support tens of millions of users across our ecosystem. Our teams operate in a fast-paced, high-impact environment alongside international leaders from companies like Glovo, Bolt, Monzo, Klarna, and Tide. If you want to build our data science function from the ground up, work with one of Africa's highest-scale fintech data sets, and ship products that influence tens of millions of users, this is an exceptional time to join. About the role We're looking for a hands on Spain-based Data Science Manager to lead our consumer credit data science efforts in a high growth environment. This role is pivotal in building and scaling a team responsible for pricing, credit limit modelling, and production credit model deployment, with direct ownership of decisions that impact millions of customers. You'll work closely with our Consumer Credit, Product, and Engineering teams to shape how we assess and price risk, design credit products, and measure outcomes across the credit lifecycle. Sitting at the intersection of data science, credit risk, and product, you'll build the analytics and modelling foundations that inform underwriting, customer acquisition, retention, and portfolio performance at scale. Developing credit scoring, affordability, and behavioural models to support underwriting, pricing, and collections Design and run experiments to optimise approval rates, loss rates, and profitability Partner with product squads to embed decision logic into real time systems Ensure data quality, compliance, and ethical use of models across all decisioning processes Mentor product squads on best practices in experimentation and data driven decision making Provide models to optimise outcomes in collections, churn management and user retention We would love to hear from you if You're comfortable with Statistics - and have a Degree or qualifications in a quantitative field (Statistics, Mathematics, Engineering or similar) You have +5 years of experience in data science, decision science, or risk analytics within financial services, including +2 years in managerment Working knowledge of credit risk, consumer lending, and regulatory considerations Proficiency in SQL and at least one modelling/programming language (Python, R) Experience with A/B testing, machine learning, collections modelling and churn management Ability to translate complex analyses into clear recommendations for business stakeholders High ownership mindset and comfort working in fast paced, cross functional teams What Moniepoint Can Offer You The opportunity to drive financial inclusion and shape the future of the African financial ecosystem The chance to work on innovative and impactful projects A dynamic, diverse, and collaborative environment where every team member's voice is recognised and valued Continuous learning and career growth opportunities Competitive salary, individual performance bonuses, and firmwide performance bonus The company covered health insurance plans Pension plans What to expect in the hiring process Introductory call with one of our recruiters Initial interview with the Director of Data Science Take home task (hands on coding or marketing modelling case study) Business case interview with our Head of Marketing Strategy & Data Live technical coding interview with the Director of Data Science Culture & values interview (60 minutes) with the Director of Data Science Moniepoint is an equal opportunity employer. We believe diversity makes us stronger and are committed to creating an inclusive environment for all employees and candidates.

About the company The the company Group connects people to experiences worth sharing, and aims to be the world's most trusted source for travel and experiences. We leverage our brands, technology, and capabilities to connect our global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. The subsidiaries of the company, Inc. (Nasdaq: TRIP), include a portfolio of travel brands and businesses, including the company, Viator, and TheFork. the company Experiences, a division of the company, is the leading marketplace for travel experiences. We believe that making memories is what travel is all about. And with 400,000+ travel experiences to explore-everything from simple tours to extreme adventures (and all the niche, interesting stuff in between)-making memories that will last a lifetime has never been easier. With industry- leading flexibility and last-minute availability, it's never too late to make any day extraordinary. The role As the Senior Director, Machine Learning, you will spearhead our Machine Learning, Data Science and Experimentation efforts, reporting to the Vice President of Engineering. You will play a pivotal role in shaping our ML and AI strategy and ensuring we leverage our data assets to their full potential to drive growth and competitive advantage. This spans the personalization, recommendation, and ranking systems that power how travelers discover and book experiences; rigorous experimentation; and the responsible application of generative AI - all built on our rich first-party data. You will run your organization as a hub-and-spoke: a strong central team that owns talent, craft standards, career growth, tooling, and long-horizon ML strategy, with scientists deployed close to the product areas they serve so delivery stays fast and grounded in real business context. You will be a strategic leader and a credible technologist: a player-coach who sets technical direction, holds the scientific bar, and stays close enough to the work to earn the trust of a demanding team. You will lead and grow the organization through a layer of managers as it scales. What you'll do Leading through a team of managers and senior scientists, you will: Build and lead a unified ML organization. Consolidate and grow ML scientists currently embedded across engineering into one hub-and-spoke model; define the operating model, decision rights, and service commitments so product areas keep momentum through the change. Own the personalization strategy. Set the technical direction for the recommendation, ranking, and search-relevance systems that personalize the traveler journey - multi-objective ranking, sequential and session-based recommenders, embeddings and semantic retrieval, and real-time features - the models that drive discovery and conversion at scale. Lead data science and experimentation. Own predictive modeling, causal inference, and the experimentation platform and standards - A/B testing, guardrail metrics, and rigorous measurement of incremental business impact - as a decision-science partner to Product and the business, championing a strong experimentation culture across the organization. Shape our generative-AI direction. Own the strategy for applying LLMs and generative AI across the Experiences product - conversational trip planning, retrieval-augmented generation grounded in our first-party reviews and content, and automated content - with rigorous evaluation to control quality and cost, including agentic and LLM-driven experiences as this work scales. Grow, develop, and retain the team. Shape ML and data-science career ladders and dual technical/management tracks; partner with Talent Acquisition to attract and hire top-tier ML and data-science talent; calibrate hiring and promotions; establish standards for the research-to-production model lifecycle; and develop both the management track and the senior IC bench - Staff and Principal Scientists. Own production ML health. Set and enforce the standards for reliable, well-monitored, cost-effective models in production - deployment, retraining, monitoring and governance - and the service levels (SLAs) of the ML organization. Partnering with MLOps infrastructure team. Partner across the company. Translate commercial strategy into an ML roadmap with Product, Engineering, Design, and Analytics leadership; represent ML in executive planning; and connect model performance to business outcomes. What you'll bring Substantial experience in machine learning, together with significant experience leading managers and senior technical teams in a complex, scaled organisation. A track record of shipping production machine learning at scale - ideally personalization, recommendations, search/ranking, or similar - to large consumer traffic, with measurable business impact. Experience building, scaling, or restructuring an ML or data-science organization, and the change-management skill to consolidate and align teams without disrupting delivery. The ability to operate as an executive peer - influencing Director- and VP-level partners across Product and Engineering and shaping strategy, not just execution. A strategic player-coach's technical depth: current enough in modern ML, experimentation, and MLOps to set direction, review architecture, and hold the scientific bar. Strong grounding in experimentation and statistics - A/B testing, causal inference, and rigorous measurement of impact. An MS or PhD in a quantitative field (Computer Science, Statistics, Mathematics, or related), or equivalent depth of applied experience. Excellent communication - the ability to translate complex ML concepts into business decisions for technical and non-technical audiences alike. Job Location: Hybrid - UK (London/Oxford) or Poland (Krakow) This role is a hybrid position that requires 2 days per week in our office What We Offer Competitive compensation packages (routinely benchmarked against the latest industry data), including base salary and annual bonuses "Work your way" with flexibility to suit your lifestyle. the company Group takes a remote-friendly approach to collaboration across a worldwide team, with the option to join on-site as often as you'd like or as required by your team. Flexible schedule. Work-life balance is ingrained in our culture by design. Trust and accountability make it work. Donation matching. Give back? Give more! We match qualifying charitable donations annually. Tuition assistance. Want to level up your career? We love to hear it! Receive annual support for qualified programs. Lifestyle benefit. An annual benefit to spend on yourself. Use it on travel, wellness, or whatever suits you. Travel perks. We believe that travel is employee development, so we provide discounts and more. Employee assistance program. We're here for you with resources and programs to help you through life's challenges. Health benefits. We offer great coverage and competitive premiums. Generous referral scheme. Help us grow and be rewarded with generous awards for referring successful candidates. Our Cultural Pillars: Traveler first We exist to create value for our customer, the traveler. We enable our suppliers and partners to unlock this value. Their collective behaviors and insights are what drives us. Execution is our edge We act fast, experiment, learn from failure, iterate, and improve the solutions of tomorrow across every aspect of our business. Our execution is agile, data-driven, prioritised, and built to scale. We assume no problem is someone else's problem and finish what can be done today, knowing tomorrow will bring fresh challenges. We succeed together The best outcomes are driven by empathic, humble, and diverse subject matter experts working toward shared goals. We collaborate relentlessly, challenge assumptions, give actionable feedback, and set each other up for success through empowered teams with a clear charter. We transparently take ownership of our growth, individually and as a team. We celebrate the quality of our effort, our learnings, and our collective achievements. We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at - If you have any additional questions about careers at the company you can email us at - We have all the answers! Find Jobs in United Kingdom on Arbeitnow

This is your opportunity to join AXIS Capital - a trusted global provider of specialty lines insurance and reinsurance. We stand apart for our outstanding client service, intelligent risk taking and superior risk adjusted returns for our shareholders. We also proudly maintain an entrepreneurial, disciplined and ethical corporate culture. As a member of AXIS, you join a team that is among the best in the industry. At AXIS, we believe that we are only as strong as our people. We strive to create an inclusive and welcoming culture where employees of all backgrounds and from all walks of life feel comfortable and empowered to be themselves. This means that we bring our whole selves to work. All qualified applicants will receive consideration for employment without regard to any protected characteristic, including age, color, disability, ethnicity, gender identity, marital status, national origin, pregnancy, race, religion, sex, sexual orientation, veteran status, or any basis prohibited by the laws that govern its operations. How does this role contribute to our collective success? Data and analytics are of critical importance for AXIS. Simply put, we want to turn data into information, that can be used to: Enable decisions to be made with confidence, based on information not just intuition. Be more proactive, using information to identify new opportunities, and getting to them before our competitors. Realize cost savings, finding ways to make processes more efficient, enabling our people to focus on using their skills to further add business value. The Data Science & AI Delivery Lead is a key leadership role within this team, acting as the deputy to the Head of Data Science & AI Delivery and ensuring the smooth day-to-day running of AI delivery operations. What will you do in this role? The Data Science & AI Delivery Lead acts as the right hand to the Head of Data Science & AI Delivery, taking ownership of the day-to-day execution and coordination of data science and AI delivery across strategic projects and standalone use cases. You will play a hands-on leadership role - driving technical delivery, managing workstreams, and stepping in for the Head of when required. This is not a purely managerial role. You will be expected to work directly in the codebase, review and shape technical designs, and maintain deep fluency with the tools and techniques the team uses daily. Working closely with the Head of, you will ensure projects are delivered to a high standard, on time, and with strong stakeholder engagement. Day-to-Day Delivery Leadership: Own the operational running of AI delivery workstreams, ensuring the team is focused, unblocked, and delivering against priorities. Act as the primary point of escalation when the Head of is unavailable. Project Management: Directly manage the design, build, test, and deployment of AI solutions across multiple concurrent projects, ensuring quality and timely delivery. Technical Oversight & Contribution: Provide technical guidance on solution design, model selection, and architecture decisions, ensuring the team is taking the right approach for each use case. Review code and technical outputs to maintain quality standards, and contribute hands on to Python-based development when needed, particularly during critical delivery phases or proof of concept work. Support the design and delivery of Generative AI solutions, including RAG architectures, prompt engineering, and integration with Azure AI services (e.g., Azure OpenAI, Azure AI Search). Ensure robust MLOps practices are followed, including model versioning, testing, deployment pipelines, and monitoring of deployed solutions for performance and drift. Champion engineering best practices across the team, including code review standards, documentation, version control (Git), and a clear separation between experimentation and production ready code. Collaboration: Work closely with AXIS's Business Technology Solutions for solution architecture and delivery, and with the Program Office for planning deliveries, providing timelines, cost estimates, and status updates. Partner with Data Engineering to ensure data pipelines and lakehouse structures support AI delivery needs. Team Development: Support the Head of Data Science & AI Delivery in leading and developing a growing team of Data Scientists and AI Engineers, fostering a culture of excellence, collaboration, and continuous learning. Mentor and coach team members through hands on pairing, structured code reviews, and knowledge sharing sessions on emerging techniques and tools. Resource & Capacity Planning: Support the Head of in tracking the project pipeline, estimating costs (including cloud compute and API consumption), and managing resource allocation to ensure the team is effectively deployed against priorities. Technology Vision: Stay current with the rapidly evolving AI landscape, including new foundation models, open source frameworks, vector databases, orchestration tools (e.g., LangChain, Semantic Kernel), and evaluation methodologies. Bring new ideas to the team and lead proof of concept development to assess feasibility and value. Deputising: Represent the Head of Data Science & AI Delivery in meetings, steering groups, and stakeholder forums as required, ensuring continuity of leadership and communication. You may also be required to take on additional duties, responsibilities, and activities appropriate to the nature of this role. This role reports to the Head of Data Science & AI Delivery. About You: We encourage you to bring your own experience and expertise to the table so, while there are some qualifications and experiences we need you to have, we are open to discussing how your individual knowledge might lend itself to fulfilling this role and help us achieve our goals. What you need to have: Demonstrable experience delivering AI/ML solutions in a professional environment, with a track record of taking projects from concept through to production. Strong technical knowledge, including proficiency in Python and experience with core data science and ML libraries (e.g., scikit-learn, pandas, PyTorch or TensorFlow). Practical experience with Databricks, including MLflow for experiment tracking and model management, and Spark-based data processing. Familiarity with Generative AI concepts and tooling, including working with LLMs, prompt engineering, RAG patterns, and vector search. A solid understanding of MLOps principles, including CI/CD for models, automated testing, and monitoring of deployed solutions. Cloud platform experience, preferably Azure, including services such as Azure OpenAI, Azure Machine Learning, or Azure AI Search. Experience managing or leading technical delivery teams, with a track record of fostering a positive, high performing team culture. Excellent communication skills and the ability to translate complex technical concepts for business stakeholders. Strong project management skills, including resource estimation, progress tracking, and meeting deadlines in a fast paced, multi priority environment. A proactive, problem solving mindset with the ability to operate independently and step up into a leadership vacuum when needed. What we prefer you have: Experience with advanced NLP techniques beyond prompt engineering, including fine tuning and evaluation frameworks for generative models. Experience working in the (re)insurance industry, with an understanding of underwriting, claims, or actuarial data. Knowledge of responsible AI practices, including bias detection, explainability, and fairness testing. Role Factors: Attend your local office at least three (3) days per week to meet and build relationships with colleagues and the wider business. Engage in company activities to grow your network and build a strong team culture. What we offer: For this position, we currently expect to offer a base salary in the range of $175,000 - $200,000 USD (New York, NY), $110,000 - $130,000 CAD (Halifax, Nova Scotia) Your salary offer will be based on an assessment of a variety of factors including your specific experience and work location. On top of this, you will be offered competitive target incentive compensation, with awards based on overall corporate and individual performance. On top of this, you will be eligible for a comprehensive and competitive benefits package which includes medical plans for you and your family, health and wellness programs, retirement plans, tuition reimbursement, paid vacation, and much more. Where this role is based in the United States of America, this role is Exempt for FLSA purposes. Rewarding. Challenging. Meaningful. We are shaping the direction of Insurance and Reinsurance during a critical and exciting time for the industry. Whether you are a student approaching graduation or a seasoned professional looking for a new environment, AXIS has the right challenges and career opportunities for you. At AXIS, we value each individual and recognize that attracting and retaining the right people is essential to the success of our company. We offer a comprehensive and competitive benefits package which includes medical plans for employees and their families, health and wellness programs, retirement plans, tuition reimbursement, paid vacation, and much more.

the company (Series B) is building the go-to financial app for the Global South. Moving to a new country shouldn't mean starting from zero. That's why our team of 400+ spanning 20+ countries is building a financial ecosystem that helps immigrants stay connected to home, build stability, and create wealth regardless of where they are from or where they live. What began as fast, affordable remittances is now evolving into a complete platform for multi-currency accounts, payments, credit, and long-term financial growth. With millions of users across the globe, we process over $1B in monthly transactions to 30+ countries, proving that borders shouldn't limit financial opportunity. the company is building the financial infrastructure for the world's migrants and underserved communities, and credit is one of our fastest-growing bets. As Collections Manager you will sit at the intersection of strategy and operations, designing the frameworks that determine how we recover debt fairly and efficiently, then rolling your sleeves up to make sure those frameworks actually work in the field. This is a backfill at a senior level, which means the foundations exist but the ceiling is high. You will own the collections function end-to end, shape policy, lead a team, and report into senior leadership. The right person is equally comfortable in a board level conversation about risk appetite and on the floor diagnosing why a workflow is breaking. If you thrive only at one end of that spectrum, this is not the role for you. What you'll build and ownDesign and continuously refine the end to end collections strategy across all product lines, balancing recovery rates with a genuinely customer first approach. Set and own portfolio level targets (roll rates, recovery rates, cure rates, cost per recovery) and hold the team accountable to them through clear, data driven governance. Build and iterate operational playbooks for each stage of the collections journey, from early arrears contact through to legal escalation, using AI tools to accelerate cycle times and personalise outreach. Lead, coach and grow a collections team, creating clear prioritisation frameworks so the right problems get worked first and critical tasks never fall through the cracks. Work cross functionally with Product, Engineering, Legal and Finance to embed collections logic into product decisions and ensure regulatory compliance across every market we operate in. Identify and integrate third party debt collection agencies, legal partners and credit bureaus where appropriate, managing those relationships for performance and conduct. Report portfolio health and strategic recommendations to senior leadership in plain language, turning data into decisions rather than decks for their own sake. Your experienceSignificant experience in consumer (B2C) collections or credit risk. Proven track record spanning both strategy design and hands on operational delivery, not just one or the other. Experience managing or building a collections team, with clear examples of setting priorities, developing people and holding performance to account. Working knowledge of FCA CONC 7 (Arrears, Default and Recovery) and Consumer Duty obligations is required, you should be able to speak fluently to how these shape treatment paths, forbearance, and vulnerable customer handling, not just cite them. Demonstrable ability to use data to diagnose portfolio problems, model scenarios and drive decisions, comfortable with SQL, BI tools or equivalent, and willing to bring AI into your workflow. Experience working in a high growth or fast moving environment where processes were incomplete and you had to build as you flew. Strong judgement about what to prioritise: able to distinguish between the problems that matter today and the noise, and able to instil that discipline in a team. Nice to haveExperience across multiple geographies or emerging markets, particularly in Africa, South Asia or the diaspora corridor. Background in a fintech, neobank or digital lender where collections was built from a relatively early stage. Familiarity with collections specific tooling (e.g. collections management systems, dialler platforms, predictive scoring models). Why Join the company?Love shouldn't be expensive, yet those working hardest for their families often face predatory fees and banking exclusion. We're changing this. At the company, you won't be just a cog in a machine. Whether designing products, scaling operations, or telling our story, you'll tackle complex challenges with real, immediate impact. Your work goes beyond metrics - it puts money back in families' pockets and offers access to the previously excluded. Join us to make a meaningful difference, where high performance is a lifeline for millions. You can connect with us on LinkedIn and Instagram and if you haven't already, download the app on the App Store or Google Play.

Job Title Safety Consultant Employment Type Permanent Reporting Line Technical Services Manager (Robotics, Safety & Vision) Location Milton Keynes, GB Role Context The Safety Consultant will play a pivotal role in supporting Omron's commitment to machinery safety, compliance, and the ongoing development of safety-related services within the UK & Ireland. This multifaceted position involves: Machinery Safety Consultancy: Delivering expert assessments and recommendations to help customers achieve compliance with EU Directives, International Standards, and local regulations while ensuring their machinery meets legal and safety norms. Local Strategic Project Management: Leading remediation and safety improvement projects, ensuring alignment with customer requirements, regulatory standards, and Omron's internal processes. Safety Service Business Development Support: Collaborating with the sales team to grow the Safety Services portfolio by identifying opportunities, developing proposals, and establishing long-term relationships with customers. Cross-Functional Collaboration: Working with internal teams, system integrators, and customers to ensure the successful implementation of safety solutions and driving process improvements across projects. Purpose of the job The Safety Consultant will predominantly deliver Machinery Safety Consultancy Services in UK and Ireland In addition, the role will imply Strategic Project Management services for remediation projects and require the ongoing support of our sales team in the development of our safety service business. Organisational context As a Safety Consultant you will serve as a critical link between technical compliance requirements and practical, customer focused solutions. This role is integral to Omron's mission of advancing safety engineering standards within industrial environments while driving the growth and success of our safety service offerings. Primary Consultancy Responsibilities Conduct in depth audits of customer machinery to assess compliance according to EN ISO12100, EN ISO13489 and subordinated standards. Perform detailed hazard identification and risk assessment across various phases of a machine's lifecycle, including design, installation, operation, and decommissioning. Develop actionable, cost effective remediation plans aligned with EU Directives and International Standards, ensuring operational efficiency without compromising safety. Prepare comprehensive reports that clearly articulate findings, assessments, and recommendations, adhering to Omron's documentation and quality standards. Act as a trusted advisor, building relationships with customers to understand their unique challenges and safety requirements. Proactively identify opportunities for engagement, either independently or in collaboration with the Omron sales team, to drive consultancy service adoption. Support the generation of well structured Safety Service proposals, leveraging established methodologies, tools, and pricing frameworks. Contribute to Omron's Quality Assurance (QA) initiatives, supporting both local and European compliance frameworks when needed. Secondary Local Project Management Responsibilities Take ownership of safety remediation projects, coordinating efforts across all stakeholders, including customers, system integrators, and internal teams. Ensure project milestones are met by maintaining clear communication, addressing challenges promptly, and fostering collaboration. Review and validate safety designs, ensuring they meet risk assessment requirements and comply with local and European regulations. Oversee the installation and validation phases of remediation projects, ensuring quality and compliance with agreed safety standards. Prepare or verify detailed Safety Requirement Specifications (SRS) and validate design documentation, including mechanical guarding and safety control systems. Provide training or guidance to stakeholders as necessary to support project success and compliance. Broader Organisational Impact Act as a key contributor to Omron's reputation for excellence in safety engineering, supporting the company's strategic goals of fostering customer trust and advancing industry safety standards. Assist in growing the Safety Service business by identifying trends, capturing customer insights, and contributing to the continuous improvement of Omron's service offerings. Support the alignment of local activities with Omron's European safety strategy, contributing to cross regional collaboration and knowledge sharing. Represent Omron at industry events, conferences, or workshops, promoting the company's expertise in safety engineering and fostering business development opportunities. The role implies a travel time of approximately 50%, primarily in UK and Ireland. Occasional assignments in other European countries may be possible. Following the focus of onsite operations the candidate should be based in South of England. Typical KPI's UK & EMEA Safety Service Budget. UK Services Budget UK Safety Products Budget UK & EMEA Overall Billings Soft KPI's as discussed with Line Manager. Accountabilities Business Strategy Implementation of the OMRON Safety Services Strategy in UK Implementation and communication of the Safety Service Business processes. Support the sales team in the identification of clear strategic remediation opportunities. Operational Ensure that Safety Services Business processes are followed as per detailed with the Functional Safety Management plan. Report all non conformances with Safety Service Business Processes. Manage local sales business expectations, report accurately on time scales and through discussion identify only the strategic and actionable opportunities that as a business should be pursued. Support QA Process. Customer Support Clearly communicate with customers and project delivery partners through all phases of the Safety Services delivery process. Understand customer needs and production processes to arrive at practical, actionable documentation. Provide clear and concise Safety Service documentation. Support Customers through discussion on the design and implementation of practical and compliant remediation solutions. Training When required assist in the delivery of Machinery Safety training. Professional and process excellence combined with clear expectation management. Continued growth of the UK Training Business External Team Alignment Support and manage Safety Services pipeline expectations with Omron UK Sales team in alignment with OSSE HQ. Work Closely with the Omron UK Safety Application Team. Cross team functional and expectation alignment. Knowledge, Experience & Skills Consultant Safety Engineer Educational requirements Bachelor or University degree / Technician Training with 10 years industrial experience. Professional Safety certification (e.g TUV) or in preparation to attain such status. Experience requirements Work experience in consulting projects for machinery safety. Work experience and knowledge of applicable EU Directives and industrial machinery safety standards (EN, ISO, etc.). Electrical / controls background associated with industrial machinery. Experience and knowledge of electrical, mechanical, hydraulic and pneumatic concepts. Experience in applying and documenting application standards for a variety of manufacturing machinery. Experience in conducting training and presenting in customer meetings. Skills Strong analytical skills and the ability to convert identifiable needs into solutions. Ability to effectively present information and respond to questions from management, customers, and authorities.

Citi is looking for an Equity Quantitative Research Analyst to join its Equity Research team in London, where you will develop and deliver differentiated quantitative investment insights across global equity markets. Working alongside senior analysts and strategists, you will own the full research pipeline from alpha model development and signal research through to client-facing publication with a focus on near-term, actionable investment solutions. This is a high-impact role for a quantitative specialist who combines deep technical expertise with the ability to translate complex findings into compelling investment narratives for institutional clients. Responsibilities Develop, enhance, and maintain quantitative equity models that identify investment opportunities across global markets, applying cross-sectional and time-series techniques to generate actionable alpha signals. Conduct macroeconomic and equity research across a defined coverage universe, distilling findings into investable theses and delivering written reports and notes for institutional audiences. Author and contribute to high-impact, client-facing research publications, translating sophisticated quantitative analysis into clear and compelling investment narratives. Engage directly with institutional investors to market research ideas, facilitate corporate access, and present data-driven insights on factor performance, sector flows, earnings trends, and portfolio risk analytics. Build and evolve financial models and valuation frameworks that support both tactical and longer-term investment recommendations. Participate in the design and development of the equity quantitative research platform, ensuring analytical rigor and consistency across the team's output. Uphold Citi's standards of ethical conduct and regulatory compliance in all business decisions, maintaining applicable licenses and adhering to relevant rules and policies. Required Qualifications & Skills PhD or postgraduate degree in a quantitative discipline such as Quantitative Finance, Statistics, Computer Science, or Engineering preferred Demonstrated experience in quantitative equity research, gained within a sell-side research team, quantitative strategy group, systematic equity investment platform, or a comparable buy-side quantitative research function Fluency in Python or R, with hands-on experience building and validating quantitative models and conducting alpha research in a production or near-production environment Deep expertise in cross-sectional and time-series modelling, factor research, and the application of statistical and machine learning techniques to equity investment research Solid understanding of equity market structure and investment processes, including factor and risk model frameworks across global markets Ability to communicate sophisticated quantitative concepts clearly to both technical and non-technical audiences, including institutional investors and senior internal stakeholders Bachelor's or university degree as a minimum educational foundation, with a master's or postgraduate qualification preferred where a PhD is not held Beneficial Skills & Qualifications Prior experience publishing client-facing investment research and engaging directly with institutional investors in a sell-side or strategy context Background in or exposure to an embedded trading desk environment, gained within a quantitative strategy or systematic investment setting Strong SQL skills combined with familiarity with Git and software engineering best practices for research platform development Experience mentoring or providing analytical oversight to junior team members within a research environment What We Offer Opportunities to grow your career locally and collaborate with colleagues across Citi's global network A hybrid working model that balances in-office collaboration with remote flexibility Investment in your professional development through training programs, mentorship, and skill-building opportunities Employee wellbeing support including medical coverage, mental health resources, parental leave, and family support programs Competitive retirement contributions and investment options to support your long-term financial goals Candidates applying for this role must be aware that it is a Certified Role, subject to the FCA and PRA Certification Regime. The Certification Regime is one element of the Individual Accountability Regime which came into effect on 7 March 2016. Under the Certification Regime, Citi UK regulated entities (Citi entities) must ensure that employees working in certain roles categorised as specified significant harm functions (Certified Roles) are assessed as fit and proper to carry out their role. Under the guidance provided by the FCA and PRA, firms should have regard to the following when assessing fitness and propriety: Honesty, integrity and reputation Financial soundness Competence and capability In order to comply with the requirements of the Certification Regime, Citi entities must take reasonable care to ensure that an employee does not perform a Certified Role without first being certified as Fit and Proper. For this reason, you will be assessed for this role against the Fit and Proper requirements, as described above. This assessment will be carried out through extensive interviews, self-disclosures, permitted criminal record checks, regulated reference checks, credit checks and other background screening checks. Job Family Group: Research Job Family: Research Analysis Time Type: Full time Most Relevant Skills Please see the requirements listed above. Other Relevant Skills For complementary skills, please see above and/or contact the recruiter. Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law. If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi. View Citi's EEO Policy Statement and the Know Your Rights poster.

Full-time, in-office - Old Street, London. £60-100k + meaningful equity 1-5 years experience The company Arctal builds structured datasets from unstructured financial documents-100,000+ PDFs (fund reports, regulatory filings, investor letters) turned into clean, queryable data that institutional buyers use for decision-making. AI agents do the reading. We build the agents. Team of 5 delivering like a team of 50. Our customers are asset managers, allocators, and financial data firms who need reliable data extracted from documents that were never meant to be machine-readable. The role Your job is to move the needle commercially. Build the pipeline. Accelerate deals. Make Arctal impossible to ignore in the market. Internal operations are already efficient-the leverage is on the GTM side. You'll own the commercial engine that turns market presence into revenue. Pipeline & Demand Generation. Own the top of funnel. LinkedIn, content, events, outbound experiments. Build repeatable systems that generate qualified inbound. You're not just "doing marketing"-you're building the commercial engine. Sales Acceleration. Work with founders to move deals faster. Prepare demo datasets, proposals, competitive positioning. Understand what makes deals close and what makes them stall. Remove friction from the sales process. Market Positioning. Help shape how Arctal shows up in the market. Craft the narrative. Produce client-facing content that lands. Make sure the right people know we exist. Operational Support. When commercial work requires coordination across delivery, tech, or product-you make it happen. But this is in service of commercial outcomes, not ops for ops' sake. This isn't an assistant role. You own commercial outcomes, not tasks. You measure yourself by pipeline generated and deals accelerated. You The commercial builder. 1-5 years at a fast-growing startup, ideally in a GTM or growth role. You've built pipeline, closed deals, or run marketing-and you want to own the whole commercial function, not just a slice of it. Builder-operator. You don't see a line between building the system and running the business. You write the automation in the morning and close the deal in the afternoon. You'd rather build the machine than turn the crank. Agent-native. Your first instinct facing a new problem is to design a system that solves it autonomously. You use AI tools to move faster-not as a novelty, but as infrastructure. You experience genuine satisfaction in automating yourself out of work. What matters: commercially minded with a bias to action. You care about revenue, not activity. You can write compelling copy, run experiments, and talk to customers. Strong written communication. Tech-curious-you use AI tools daily. What this isn't An assistant role (you own outcomes, not tasks) A pure marketing role (you'll touch the whole commercial cycle) A 9-to-5 (intensity is high, learning is faster) Founders Aleksi (CEO) - Cambridge engineering + ML. Co-founded Secondmind, founding team at Sylvera. Previously worked on ML with Carl Rasmussen at Cambridge. Krista (CCO) - Former Head of Market Intelligence at Climate Bonds Initiative. Deep sustainable finance and capital markets expertise. Small team that ships fast. No layers, no politics-just building. What you get Old Street office, 2 min from the station. Direct exposure to customers and investors from week one. A team that ships fast and doesn't do meetings for the sake of meetings. For the right person, this becomes a commercial leadership role as we scale.