Job Description
Amazon is looking for an Sr. Applied Scientist to help build next generation selection/assortment systems. On the Selection team within the Supply Chain Optimization Technologies (SCOT) organization, we own the selection of the products that Amazon offers world-wide and what is prioritized for our fastest delivery speeds. The selection is generated with a series of Machine Learning (ML) and optimization models accounting for profitability, customer intents, and limited warehouse space. 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 a Sr. 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:
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
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
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.
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit . This position will remain posted until filled. Applicants should apply via our internal or external career site.
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