Applied Scientist, Customer Behavior Analytics

Key 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.

Basic Qualifications

• 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

Preferred Qualifications

• 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