Sr. Applied Scientist , Stores Economics and Sciences

About the role

Amazon's Stores Economics and Science (SEAS) team is looking for an Applied Scientist III to help us find global optima where others settle for local ones. You will work alongside scientists and engineers with backgrounds spanning machine learning, natural language processing, information retrieval, statistics, economics, causal inference, and optimization to lower cost-to-serve, optimize selection, and apply emerging machine learning techniques across a wide range of Stores problems - all at a scale that directly shapes how customers shop.

This is a role for someone who does not default to the most popular model but instead thinks carefully about which approach will produce the most reliable and scalable result for each problem. You will lead science initiatives end-to-end, translating complex business problems into mathematical frameworks, building large-scale algorithms, and deploying production solutions in partnership with product teams. If you want to do cross-discipline applied science that moves real metrics, we would like to talk.

Key job responsibilities

• Lead large-scale science initiatives from research through production, translating supply chain and marketplace problems into mathematical frameworks and deploying algorithms that delight Amazon customers.
• Design and implement machine learning and statistical models - choosing the right method for each problem rather than defaulting to a single paradigm - and take ownership of their performance in production.
• Drive your team's scientific agenda by identifying new research opportunities, proposing initiatives, and aligning with technical leadership on priorities.
• Collaborate with scientists, engineers, and product teams across Amazon's Stores organization to prototype, validate, and scale solutions that accelerate partner teams' progress.
• Mentor and coach fellow scientists through code reviews, design reviews, and authoring of internal technical documents and external publications.

A day in the life

• You might start your morning analyzing experiment results from a model you recently deployed, deciding whether the estimates are robust enough to move to an online test.
• By mid-morning you are whiteboarding a new formulation with a colleague from a partner team.
• After lunch you review a pull request, suggesting a simpler algorithmic approach that cuts latency.
• Later you carve out time to write code for a prototype that tests a new idea for reducing cost-to-serve.
• Your work regularly moves between hands-on modeling and the collaborative work of aligning your team around a shared scientific roadmap.

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Additional information

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.

Pay and benefits

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.

USA, WA, Seattle - 167,100.00 - 226,100.00 USD annually