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 Amazon's 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.
BASIC QUALIFICATIONS
- 3+ years of building models for business application experience
- PhD, or Master's 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
PREFERRED QUALIFICATIONS
- Experience using Unix/Linux
- Experience in professional software development
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 - 142,800.00 - 193,200.00 USD annually
