Data Scientist, Sales AI

Amazon Amazon · Big Tech · NY +1 · Machine Learning Science

This role focuses on building and applying LLM-based solutions, including RAG and agentic workflows, to solve Ad Sales problems and generate insights. It involves translating business problems into data science tasks, building statistical and ML models, analyzing large datasets, designing experiments, and moving solutions into production. The role also requires collaboration with applied scientists and engineers, and presenting findings to various stakeholders.

What you'd actually do

  1. Build and apply LLM-based solutions (retrieval-augmented generation, prompting, and agentic workflows) to solve Ad Sales problems and generate seller-facing insights and recommendations.
  2. Translate ambiguous business problems into data science problems, and build the statistical and machine learning models that complement LLM workflows, such as ranking, retrieval evaluation, segmentation, and forecasting.
  3. Analyze large, complex datasets, including conversation and LLM interaction data, to surface insights, quantify gaps, and identify opportunities to improve our generative AI products.
  4. Design and run experiments (A/B tests, offline and online evaluations, causal inference) and define metrics that measure the quality, accuracy, and business impact of LLM-driven solutions.
  5. Write code (Python, SQL) to acquire, transform, and analyze data at scale, and partner with applied scientists and engineers to move solutions into production.

Skills

Required

  • Python
  • SQL
  • statistical modeling
  • machine learning
  • experimentation
  • causal inference
  • data querying languages
  • scripting languages
  • machine learning/statistical modeling data analysis tools and techniques
  • working with or evaluating AI systems
  • applying theoretical models in an applied environment

Nice to have

  • Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
  • knowledge of machine learning concepts and their application to reasoning and problem-solving
  • Perl
  • ML or data scientist role with a large technology company
  • defining and creating benchmarks for assessing GenAI model performance
  • multi-team, cross-disciplinary projects
  • applying quantitative analysis to solve business problems and making data-driven business decisions
  • effectively communicating complex concepts through written and verbal communication

What the JD emphasized

  • LLM-based solutions
  • generative AI
  • agentic workflows
  • retrieval-augmented generation

Other signals

  • LLM-based solutions
  • generative AI
  • agentic workflows
  • retrieval-augmented generation