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Description

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.

PXT Central Science (PXTCS) is Amazon's internal research organization dedicated to bringing scientific rigor to people and workforce decisions at global scale. Our team sits within the part of PXTCS that focuses on Amazon's Tier 1 hourly populations — the associates at the heart of Amazon's operations. We are a multidisciplinary group of 15 economists, data scientists, data engineers, and research scientists united by a single mission: to transform complex operational challenges into actionable insights through rigorous causal analysis and predictive modeling that empowers data-driven workforce decisions.

We are building something new — causal predictive models that go beyond traditional forecasting. Our models don't just tell leaders what will happen; they reveal why it will happen and what levers they can pull to change the outcome. This is the frontier where causal inference meets modern machine learning, and we need a scientist who can help us push it forward.
As a Senior Applied Scientist on this team, you will be the connective tissue between innovative research and real-world impact. You will work shoulder-to-shoulder with economists who deeply understand the causal mechanisms driving workforce dynamics and data scientists who know the operational landscape — and you will bring the technical creativity to expand what's possible. That means writing production-quality code that our partner engineering teams can implement into decision-making tools. It means exploring novel feature spaces — large language models, computer vision, and other emerging techniques — to unlock signal that traditional approaches miss. And it means doing all of this with the scientific rigor that causal claims demand.

This role is built for someone who is entrepreneurial and energized by ambiguity — someone who sees a prototype model and immediately starts thinking about how to make it robust, scalable, and impactful. You will not just advance your own work; you will elevate the scientists around you. We are looking for a strong technical individual contributor who is passionate about developing peers, raising the bar across disciplines, and who sees a future path into a science manager position.

If you want to do science that directly shapes how Amazon supports its workforce — not in theory, but in production systems that leaders use to make better decisions every day — we'd love to talk.


Key job responsibilities
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 your 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.

About the team
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.