Sr. Applied Scientist, PXT Science

Every day, hundreds of thousands of Amazon associates show up to fulfill the promise we make to our customers. Behind the workforce decisions that support them—staffing, retention, scheduling, development—there should be science that doesn't just describe what happened, but explains why it happened and predicts what comes next. That's the work we do.

• Design and build causal predictive models that move beyond correlation—developing systems that forecast workforce outcomes and identify the actionable drivers behind them, enabling leaders to intervene before problems materialize.
• Pioneer novel feature engineering by bringing creative approaches from LLMs, computer vision, and other emerging techniques into the causal modeling pipeline, unlocking signal that traditional econometric and tabular methods miss.
• Write production‑quality science code that the partner engineering team can implement directly into operational decision‑making tools—your work must be clean, well‑documented, and built to scale.
• Bridge disciplines by translating between economists, data scientists, and engineers—synthesizing causal rigor with ML innovation to produce models that are both scientifically defensible and operationally useful.
• Design and execute experiments to validate causal claims and model performance, establishing evaluation standards that the team and stakeholders trust.
• Develop and elevate peers across the team—mentoring scientists in adjacent disciplines, sharing technical knowledge, and raising the collective bar on modeling and engineering practices.
• Present findings to senior leadership, distilling complex causal and predictive insights into clear recommendations that drive workforce strategy for Amazon's Tier 1 hourly populations.

Amazon’s People Experience and Technology Central Science (PXTCS) team uses economics, behavioral science, statistics, machine learning, applied science, and Generative AI to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well‑being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science, engineering, and UX to develop and deliver solutions that measurably achieve this goal.

• PhD, or Master’s degree and 6+ years of applied research experience.
• 5+ years of building machine learning models for business application experience.
• Experience programming in Java, C++, Python or related language.
• Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc.

• PhD in econometrics, statistics, industrial engineering, operations research, optimization, data mining, analytics, or equivalent quantitative field.
• Experience with neural deep learning methods and machine learning.
• Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi‑experiments, and data science workflows.
• Experience in taking a product from conception & definition phase through engineering design and taking it to market.
• Experience working with emerging technologies.
• Experience in mentoring, leading and coaching.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Los Angeles County applicants:
Job duties for this position include work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position.

These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism,…