Applied Scientist, Customer Behavior Analytics

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Scientist role focused on designing and developing machine learning solutions for customer behavior analytics, utilizing deep learning, LLMs, recommendation systems, and reinforcement learning. Key responsibilities include fine-tuning generative models, developing recommendation and decision models, building behavioral representations, applying post-training optimization, and creating evaluation frameworks. The role emphasizes measurable business impact and customer satisfaction.

What you'd actually do

  1. Design and fine-tune language and generative models for recommendation and engagement, including continued pre-training, supervised fine-tuning, and preference-based alignment, to optimize for long-term customer value rather than short-term clicks.
  2. Develop generative recommendation and decision models that produce next-best customer engagement actions (e.g., recommendations, bundles, messaging, incentives, timing), conditioned on structured customer and household-level behavioral context.
  3. Build structured, temporal representations of customer behavior (e.g., lifecycle stage, needs, replenishment patterns, engagement history) and integrate them into generative and deep learning models to enable long-horizon reasoning.
  4. Experiment scalable representations of customer and household behavior that summarize long engagement history into compact states, supporting efficient, incremental inference in large-scale inference.
  5. Design and apply post-training optimization techniques (e.g., auxiliary objectives, preference modeling, offline reinforcement learning or policy optimization) to align model behavior with long-term engagement, satisfaction, and retention metrics.

Skills

Required

  • building models for business application
  • PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
  • Experience in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience programming in Java, C++, Python or related language
  • Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
  • PhD, or Master's degree and 4+ years of practical machine learning experience
  • Experience communicating results to senior leadership

Nice to have

  • PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
  • Experience building machine learning models or developing algorithms for business application
  • Experience in designing experiments and statistical analysis of results
  • Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

What the JD emphasized

  • state-of-the-art technology
  • state-of-the-art models
  • state-of-the-art deep learning models architecture design

Other signals

  • design and fine-tune language and generative models
  • develop generative recommendation and decision models
  • build structured, temporal representations of customer behavior
  • design and apply post-training optimization techniques
  • develop robust evaluation frameworks