Research Scientist, Operational Efficiency, AET Planning and Analytics Science

Research Scientist, Operational Efficiency, AET Planning and Analytics Science

Job ID: 10447029 | Amazon.com Services LLC

We're looking for a Research Scientist to join a team that builds the science behind how Amazon supports its 1.5M+ employees - from forecasting demand across HR services to optimizing how work gets routed and assigned in real time. You'll own high‑impact operations research and causal inference work that directly reduces costs and improves the employee experience at global scale.

Responsibilities

• Design and build simulation and optimization models that automate workforce scheduling, hiring, and task assignment across Amazon's HR contact centers and back‑office operations.
• Develop causal inference frameworks to measure the true impact of policy changes, product launches, and AI‑driven initiatives on employee experience and operational efficiency.
• Collaborate with senior leaders to translate complex analytical findings into actionable strategies that influence staffing, routing, and resource allocation decisions affecting thousands of associates.
• Push the boundaries of what's possible by combining operations research with machine learning and generative AI to solve novel workforce optimization problems.

You'll build novel solutions such as contact center simulators, scheduling optimizers, and experiment frameworks that credibly quantify the impact of large‑scale organizational changes. Your work will directly inform decisions made by VP and Director‑level leaders who set Amazon’s strategy for employee support at scale.

About the team

We are a team of economists, research scientists, and data scientists within Amazon's People Experience and Technology Finance organization. Our mission is to make Amazon's employee support services smarter, faster, and more efficient through rigorous science – from forecasting models that drive hiring plans to simulation engines that optimize how HR services are delivered to Amazon's global workforce of 1.5M+ people.

Our work runs in production, informs weekly planning decisions, and shapes resource allocation at scale. We value intellectual rigor, ownership, and collaboration – publishing at internal science conferences, maintaining shared codebases, and holding each other to high technical standards. You’ll have real ownership from day one: framing problems, building solutions, and presenting results to senior leaders who act on your recommendations.

Basic Qualifications

• PhD, or Master’s degree and 4+ years of quantitative field research experience.
• Experience with statistical modeling / machine learning.
• Knowledge of R, MATLAB, Python or similar scripting language.

Preferred Qualifications

• Experience investigating the feasibility of applying scientific principles and concepts to business problems and products.
• Experience in causal modeling like graphical models, causal Bayesian network, potential outcomes, A/B testing, experiments, quasi‑experiments, and data science workflows.
• Experience consulting with senior leadership and executives in a fast‑paced environment.

Salary and Location

• USA, VA, Arlington: $136,000.00 - $184,000.00 USD annually
• USA, WA, Bellevue: $136,000.00 - $184,000.00 USD annually
• USA, WA, Seattle: $136,000.00 - $184,000.00 USD annually

Benefits

The base salary range for this position is listed above. 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

Equal Opportunity

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