Amazon is looking for an Applied Scientist to help build next generation selection/assortment systems. On the Specialized Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection of the products that Amazon offers in our fastest delivery programs world-wide. The selection is generated with a series of Machine Learning (ML) and optimization models to best cater to customer purchase intents under limited warehouse capacity. We build tools and systems that enable our partners and business owners to scale themselves by leveraging our problem domain expertise, focusing instead on introspecting our outputs and iteratively helping us improve our models rather than hand-managing their assortment. We partner closely with our business stakeholders as we work to develop state-of-the-art, scalable, automated selection management systems.

As an Applied Scientist, you will work with software engineers, product managers, and business teams to understand the business problems and requirements, distill that understanding to crisply define the problem, and design and develop innovative solutions to address them. Our team is highly cross-functional and employs a wide array of scientific tools and techniques to solve key challenges, including supervised and unsupervised machine learning, large language models, linear, nonlinear, and non-convex optimizations, reinforcement learning, causal inference, and experiment designs. Some critical research areas in our space include modeling substitutability between similar products, complementarity and basket building, measuring speed sensitivity of products through experiments, optimizing assortment under operational and capacity constraints, and supply and demand forecasting.

Find out more about SCOT at Amazon: http://bit.ly/amazon-scot

Key job responsibilities
You will be an end-to-end owner for the projects you support. Responsibilities include:
- Understanding business requirements and existing challenges and map them to the right scientific solution;
- Designing effective, scalable, and achievable solutions to key business problems;
- Developing the right set of metrics to evaluate efficacy of your models and solutions;
- Prototyping and analyzing new models and business logic;
- Productionizing your scientific solutions, including writing production-quality critical path code;
- Communicating, both written and verbally, with both technical and business audiences throughout each project;
- Publishing findings in internal and/or external conferences and interfacing with the scientific community;
- Mentoring and developing the scientist community across the organization