Amazon.com Services LLC
Applied Science, operations
Part-timeAppliedScientist,Labs,SCOTForecastingandLabs
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Part-time Applied Scientist, Labs, SCOT Forecasting and Labs at Amazon.com Services LLC. Skills: Causal inference, Machine learning, Supply chain. Advance causal inference methodology. Support methods with theoretical foundations”
What You'll Achieve.
Accelerate policy evaluation; Assess policy impact faster; Assess policy impact more accurately; Influence policy decisions; Influence inventory investments; Influence labor allocation; Improve supply chain efficiency
Industry & Context.
Causal inference challenges
What They're Looking For.
Must Have
3+ years ML models business application, Master's degree and 5+ years applied research, Programming in Java, C++, Python, Research in corporate setting
Nice to Have
Neural deep learning methods experience, Machine learning experience, Large scale distributed systems experience, Hadoop experience, Spark experience
What You'll Do.
Advance causal inference methodology
Support methods with theoretical foundations
Support methods with empirical evidence
Translate research findings into insights
Translate research findings into recommendations
Contribute to Amazon's scientific community
Contribute to external research field
Mentor fellow scientists
Provide technical guidance
Foster scientific rigor
How You'll Work.
Team & Collaboration
Cross-functional teams; Technical stakeholders; Non-technical stakeholders
Communication Scope
Clear insights; Actionable recommendations; Publication
Full 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 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 - 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 ev
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