Applied Scientist, Customer Behavior Analytics

Amazon • Seattle, WA

4d • Onsite

About The Position

The Customer Behavior Analytics team designs innovative machine learning solutions to enhance customer experiences and strengthen their relationship with Amazon. This interdisciplinary team of scientists and engineers incubates and develops disruptive solutions using state-of-the-art technology to tackle some of the most challenging scientific problems in customer behavior analysis at Amazon. To achieve this, the team utilizes methods from deep learning, large language models (LLMs), natural language models, recommendation systems, affinity models, reinforcement learning, and econometrics to drive personalized experiences throughout the customer journey.

As a Customer Behavior Analytics Scientist, you will have the opportunity to make a significant business impact, delve into large-scale problems, drive measurable actions, and collaborate closely with other scientists and engineers. You will be responsible for designing and developing state-of-the-art models and working with business, marketing, and engineering teams to address key challenges in customer behavior analytics.

In this role, you will be an analytical problem solver who enjoys exploring data, participating in problem-solving efforts, developing new frameworks, and engaging in investigations and algorithm development. You should be capable of effectively collaborating with technical teams and business stakeholders, pushing the boundaries of what is scientifically possible, and maintaining a sharp focus on measurable customer satisfaction and business impact.

Your work will be crucial in shaping the future of customer behavior analytics at Amazon, driving innovation that directly impacts millions of customers worldwide. This position offers a high-visibility opportunity to contribute to solutions that are vital to improving customer satisfaction and loyalty, serving as a model for customer-centric solutions across the company.

Requirements

• 3+ years of building models for business application experience
• 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, or experience building and managing financial models for business forecasting and problem solving

Nice To Haves

• 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

Responsibilities

• 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.
• 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.
• 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.
• Experiment scalable representations of customer and household behavior that summarize long engagement history into compact states, supporting efficient, incremental inference in large-scale inference.
• 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.
• Develop robust evaluation frameworks combining offline metrics, counterfactual analysis, and online experimentation to measure both immediate impact and long-term customer outcomes.

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior