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 worldwide 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 handmanaging their assortment. We partner closely with our business stakeholders as we work to develop stateoftheart 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 crossfunctional 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 nonconvex 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: job responsibilities
You will be an endtoend 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 productionquality 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
3 years of building machine learning models for business application experience
PhD or Masters degree and 6 years of applied research experience
Experience programming in Java C Python or related language
Experience with modeling tools such as R scikitlearn Spark MLLib MxNet Tensorflow numpy scipy etc.
Experience with large scale distributed systems such as Hadoop Spark etc.
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Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150400/year in our lowest geographic market up to $260000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on jobrelated knowledge skills and experience. Amazon is a total compensation company. Dependent on the position offered equity signon 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.