Alldus International Consulting Ltd

Engineering Manager - Data Science & Machine Learning Location: London, UK Are you a Data Science or Machine Learning Manager tired of the lack of investment or buy in from your executives? Or searching for that excitement again when building products that you can directly see tangible outcomes? If yes, check out the below: An industry leading org built around fairness and sustainability within adtechis building products that can handle 400 billion auctions per day. Exceeding that of any Google or Amazon programmatic ad marketplace.The ML team is building products that help increase latency and speed. A globally distributed team with the bulk of the Data Science & Machine Learning org in Berlin. Their Executives call the ML team the "secret sauce" to their evolution. What's in it for you? Salary £140-160k Hybrid Working environment Build at Scale - work on products that overshadow Google and Amazon searchscales AND see direct impact through visible KPI's Buy in from Execs - No headaches around trying to get the smallest thing approved. Work in an environment that believes in constantly innovating with a product mindset (iterate , test then build) Progression - room to grow into a senior and then director. Sounds good? Check the below to see how to be successful in this role What will you need to be successful? As the Engineering Manager you will be the right hand of the VP of ML. To succeed in this role you need thecore 4 skills. 1. People Leadership: Get into the heads of your engineers understand their strengths and weaknesses, empower them , grow them understand how changes can benefit them. You needto know how to make your team tick in sync and proven experience of doing this before. 2. Processes: They are no longer a startup, moving from scrappy to self-sufficient &organized is a big goal for this Data Science team. Getting your team processes structuredand self sufficient is a key piece of the puzzle for this hire to be successful. 3. Data Science & ML: You need to know how these models work and all the different variables that can go wrong. Models outputs are not always 100% accurate. While not expecting you haveSpidey like senses the data science intuition of knowing that a models output may be missing somethingis really critical and saves them alot of 's. 4. Engineering: While any ML org would love just to do R+D the team have to make money. So having successfully built and launched products in Machine learning is the final core skill for the Engineering Manager. Understand the challenges and how to of getting Data Science and ML models into production and the lifecycle of an ML product will help you achieve success in this role. If you think you could thrive in this role get in touch via apply or drop me an email at 53722