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 productionizes 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.
Key job responsibilities
• 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 teams
• Bridge economists, data scientists, research scientists, and engineers - synthesizing causal rigor with ML innovation to produce models that are scientifically defensible and operationally useful
• Partner with stakeholders and senior leadership to define priorities, drive adoption of science-informed workforce strategy, and leverage the broader scientific community
• Distill complex causal and predictive findings into clear recommendations for senior leadership that drive workforce strategy for Amazon's hourly populations
• Define team structure, strategic direction, and owned technologies, adjusting priorities and removing roadblocks to optimize outcomes
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.
BASIC QUALIFICATIONS
- 3+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
PREFERRED QUALIFICATIONS
- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, CA, San Francisco - 211,400.00 - 286,000.00 USD annually
USA, VA, Arlington - 183,800.00 - 248,700.00 USD annually
USA, WA, Seattle - 183,800.00 - 248,700.00 USD annually
