Manager III, Applied Science, PXT Science

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 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 leader who can build and guide the team that pushes it forward.

As an Applied Science Manager on this team, you will own both the scientific vision and the people strategy for our applied science function. You will lead a diverse team of scientists working at the intersection of causal inference and machine learning — setting the technical direction, raising the bar on modeling and engineering practices, and ensuring that research translates into production systems that leaders use to make better workforce decisions every day.

You will work closely with economists who deeply understand the causal mechanisms driving workforce dynamics, data scientists who know the operational landscape, and a dedicated partner engineering team that product ionizes your team's work.

This is not a role where you manage from a distance. You will stay close to the science — reviewing model designs, shaping feature engineering strategies, and guiding your team through the ambiguity of novel problem spaces including large language models, computer vision, and other emerging techniques applied to workforce challenges. At the same time, you will build the team culture, operating mechanisms, and talent pipeline needed to scale our applied science capabilities as the organization grows.

This role is built for someone who is both a strong technical scientist and a genuine people leader — someone who gets energy from developing others, who can translate between disciplines, and who sees building a high-performing team as one of the most impactful things they can do. You will partner with stakeholders and senior leadership to define priorities, communicate results, and drive the adoption of science-informed workforce strategy across Amazon's operations.

If you want to lead a team doing science that directly shapes how Amazon supports its workforce — not in theory, but in production systems that drive real decisions at scale — we'd love to talk.

• Manage and develop a high-performing team of scientists — fostering innovation and scientific rigor while providing coaching, mentorship, and clear growth paths
• Establish operating mechanisms and performance expectations to track and communicate team progress
• Own hiring and talent strategy for the applied science function, including hiring and conversion for other job families
• Set and execute the scientific vision for the applied science function — bringing deep ML expertise to the team's causal predictive modeling agenda and identifying where advanced methods (deep learning, LLMs, computer vision, novel architectures) can strengthen the causal frameworks and unlock signal that traditional approaches miss
• Establish standards for code quality, documentation, and scalability to ensure your team's work can be implemented directly into operational decision‑making tools by partner engineering…