Part-time Applied Scientist, Labs, SCOT Forecasting and Labs

Amazon•New York, NY

4d•$183,800 - $248,700•Onsite

About The Position

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.

Requirements

3+ years of building machine learning models for business application experience
PhD, or Master's degree and 5+ years of applied research experience
Experience programming in Java, C++, Python or related language
Experience with conducting research in a corporate setting

Nice To Haves

Experience with neural deep learning methods and machine learning
Experience with large scale distributed systems such as Hadoop, Spark etc.

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 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.
Mentor and develop fellow scientists by providing technical guidance, reviewing research, and fostering a culture of scientific rigor across the team.