Job Description
As a Research Analyst II (RA), you'll collaborate with experts to develop GenAI, LLM and ML solutions for business needs. You'll drive product pilots, demonstrating innovative thinking and customer focus. You'll build scalable solutions, write high-quality code, and develop state-of-the-art LLM models. You'll coordinate between science and software teams, optimizing solutions. The role requires thriving in ambiguous, fast-paced environments and working independently with GenAI models. This will require collaboration with local and global teams, which have process and technical expertise. Therefore, RA should be a self-starter who is passionate about discovering and solving complicated problems, learning complex systems, working with numbers, and organizing and communicating data and reports.
Key job responsibilities
• Scoping, driving and delivering complex projects across multiple teams.
• Performs root cause analysis by understand the data need, get data / pull the data and analyze it to form the hypothesis and validate it using data.
• Conducting a thorough analysis of large datasets to identify patterns, trends, and insights that can inform the development of NLP applications.
• Developing and implementing machine learning models and deep learning architectures to improve NLP systems.
• Collaborate with seasoned Applied Scientists and propose best in class LLM solutions for business requirements
• Dive deep to drive product pilots, demonstrate think big and customer obsession LPs to steer the product roadmap
• Build scalable solutions in partnership with Applied Scientists by developing technical intuition to write high quality code and develop state of the art ML models utilizing most recent research breakthroughs in academia and industry
• Coordinate design efforts between Sciences and Software teams to deliver optimized solutions
• Ability to thrive in an ambiguous, uncertain and fast moving ML usecase developments.
• Mentor Junior Research Analyst (RAs) and contribute to RA hiring
About the team
The RBS Size & Fit program is a global initiative aimed at solving customer pain points related to size and fitment across Amazon's worldwide non-media catalog. Our vision is to enable customers to confidently shop the right size, every single time by defining what "size" means from a customer perspective, ensuring availability of accurate and defect-free size information, and building customer experiences (CX) that surface this information effectively. The program focuses on four pillars: (i) defining a customer-centric size construct in partnership with category leaders, (ii) building scalable and high-quality data sourcing pipelines leveraging ML and tech platforms, (iii) collaborating with CX teams to design size/fitment-specific customer experiences, and (iv) reducing catalog size defects via automated and manual correction mechanisms.
- • Bachelor's degree in Quantitative or STEM disciplines (Science, Technology, Engineering, Mathematics)
- • 3+ years of relevant work experience in solving real world business problems using machine learning, deep learning, data mining and statistical algorithms
- • Strong hands-on programming skills in Python, SQL, Hadoop/Hive. Additional knowledge of Spark, Scala, R, Java desired but not mandatory
- • Strong analytical thinking
- • Ability to creatively solve business problems, innovating new approaches where required and articulating ideas to a wide range of audiences using strong data, written and verbal communication skills
- • Ability to collaborate effectively across multiple teams and stakeholders, including development teams, product management and operations.
- • Master's degree with specialization in ML, NLP or Computer Vision preferred
- • 3+ years relevant work experience in a related field/s (project management, customer advocate, product owner, engineering, business analysis)
- • Diverse experience will be favored eg. a mix of experience across different roles - In-depth understanding of machine learning concepts including developing models and tuning the hyper-parameters, as well as deploying models and building ML service - Technical expertise, experience in Data science, ML and Statistics
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