Data Scientist

- 3+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- 3+ years of data scientist experience
- Bachelor's degree
- Master's degree, or PhD
- Natural curiosity and desire to learn

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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, WA, Bellevue - 136,000.00 - 184,000.00 USD annually
At Amazon, our SCOT Labs team owns and operates the experimentation platform that powers randomized controlled trials (RCTs) across Supply Chain Optimization Technologies (SCOT). We are the scientific gatekeepers for policy updates that govern how Amazon buys, stores, and moves billions of units of inventory worldwide. This is not traditional A/B testing: we are building the infrastructure and methodology to causally evaluate complex and interconnected supply chain interventions.

Our platform runs experiments that span millions of products and hundreds of fulfillment nodes simultaneously, measuring the real-world impact of policy changes on inventory health, customer experience, and operational cost. We are also advancing the science of causal inference in supply chain settings by developing novel approaches to treatment effect estimation, interference modeling, and emulation techniques that allow us to assess policy impact faster and more accurately than ever before.

The experiments you design and the methods you build here will directly determine which policies ship to production. These decisions influence hundreds of millions of dollars in weekly inventory investments, labor allocation for tens of thousands of associates, and Amazon's overall supply chain efficiency. Beyond operational impact, this team pushes the frontier of causal experimentation methodology and contributes to the broader scientific community with publications at top venues.

If you are a scientist who wants to shape how one of the world's largest supply chains makes decisions — solving causal inference challenges in real-world settings no academic lab or startup can replicate — this is the team for you.




Key job responsibilities
- Partner with customer teams to design rigorous large-scale experiments (such as randomized controlled trials and quasi-experiments) to evaluate policy updates and model improvements across millions of products, hundreds of fulfillment nodes, and diverse business contexts
- Lead the end-to-end experimentation lifecycle, from hypothesis formulation through analysis and stakeholder alignment, to inform production rollout decisions
- Advance causal inference methodology for supply chain settings, including treatment effect estimation, interference modeling, and emulation techniques that accelerate policy evaluation
- Build and maintain production-grade experimentation infrastructure and analytical tools using Python, SQL, Scala, and related technologies
- Perform large-scale exploratory data analysis to uncover patterns, identify opportunities, and inform experimental design and policy development
- Develop and scale supply chain emulation systems that model inventory dynamics end to end, enabling rapid offline evaluation of policy changes across millions of products without the cost and latency of live experiments
- Translate complex research findings into clear insights and recommendations for technical and non-technical stakeholders at all levels
- Contribute to Amazon's scientific community and the broader research field through collaboration and publication in top-tier venues

A day in the life
You might start the morning reviewing results from a randomized controlled trial running across millions of products, digging into causal estimates and designing the next iteration. Later, you could be designing an experiment with a partner team where interference is unavoidable: treated and control units share fulfillment networks and inventory pools, and you need a credible strategy despite the spillover effects.

You'll build supply chain emulation systems that replicate inventory dynamics end to end, write code in Python, Scala, and SQL at a scale most scientists never encounter, and collaborate with scientists, engineers, and business teams across SCOT. Your research has a real chance of being published at top venues.

The work is hard, the problems are unsolved, and the impact is immediate. If you want to do research that ships, this is where you do it.

About the team
The Forecasting and Labs Science team sits at the heart of Amazon's supply chain, building the science that determines what products are available, when, and at what cost for hundreds of millions of customers around the world. Our mission spans two deeply connected frontiers: pushing the boundaries of large-scale time series forecasting through foundation models that generalize across an enormous and diverse catalog of products, and building the experimentation and causal inference methodology that rigorously evaluates whether supply chain policy changes should ship to production.

We are a team of scientists who care deeply about both research rigor and real-world outcomes. We don't just publish: we ship. And we don't just ship: we measure, iterate, and raise the bar. On the forecasting side, we build foundation models at a scale unmatched in industry, running experiments across millions of products and exploring novel data generation techniques that open new frontiers in model generalization. On the experimentation side, we design and run randomized controlled trials across hundreds of fulfillment nodes, advance causal inference in settings where interference is unavoidable, and build supply chain emulation systems that can evaluate policy changes in hours rather than months. Our work spans the full lifecycle: from foundational research and large-scale experimentation to production deployment and downstream impact measurement across supply chain, inventory, and financial planning.