Job Description

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 Amazons 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 worlds 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
- Build and maintain production-grade experimentation infrastructure and analytical tools using Python SQL Scala and related technologies
- Advance causal inference methodology for supply chain settings including treatment effect estimation interference modeling and emulation techniques that accelerate policy evaluation. Ensure these methods are supported by both theoretical foundations and empirical evidence.
- Translate complex research findings into clear insights and actionable recommendations for technical and non-technical stakeholders at all levels.
- Contribute to Amazons scientific community and the broader external research field through collaboration and publication in top-tier venues.

A day in the life
You might start the morning digging into results from a randomized trial running across millions of products deciding whether the estimator you built last month is holding up and where it breaks. By the afternoon you could be designing the next experiment with a partner team where clean randomization is not available because treated and control units draw on the same fulfillment network and inventory pools and you need an approach that stays credible despite the spillover.

The role sits between developing methods and shipping them. You will build estimators and experiment designs for problems without textbook answers prototype them in Python on real supply chain experiment data and then put the ones that work into the platform other SCOT scientists use writing Python Scala and SQL at a scale most scientists never encounter. You will also build supply chain emulation systems that replicate inventory dynamics end to end so a design can be stress tested before it touches the real network.

The problems are open the data is unmatched in scale and the methods you build will shape how scientists across SCOT think about causal inference and move billions of dollars in inventory decisions. If you want to do measurement and experimentation research that actually ships this is where you do it.

About the team
The Forecasting and Labs Science team sits at the heart of Amazons 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 dont just publish: we ship. And we dont 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.

- 3 years of building models for business application experience
- PhD or Masters degree and 4 years of CS CE ML or related field experience
- Experience programming in Java C Python or related language
- Experience in any of the following areas: algorithms and data structures parsing numerical optimization data mining parallel and distributed computing high-performance computing

- Experience using Unix/Linux
- Experience in professional software development

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

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

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 WA Bellevue - 142800.00 - 193200.00 USD annually

Required Experience:

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Job Tags

Full time, Worldwide, Flexible hours, Day shift

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