Applied Scientist

Job ID: 1152257 | Services LLC


Amazon is looking for an outstanding applied scientist to help build next generation selection/assortment systems. As an ML scientist you will work with software engineers, product managers and business teams to understand the requirements/current challenges, distill that understanding to elegantly define the problem and develop innovate solutions to address those problems using techniques in machine learning and optimization. You will work with a team of engineers and scientists who are passionate about using machine learning to build automated systems and solve problems that matter to our customers. Your work will directly impact our customers in form of selection we offer them.

-Research and implement machine learning and optimization models to solve problems that matter to our customers
-Understand business requirements and existing challenges and map them to the right scientific solution
-Own end-to-end solution in terms of research, prototyping, experimentation to eventual roll-out
-Develop the right set of metrics to evaluate efficiency/accuracy of the algorithms
-Mentor and develop the scientist community across the organization

Amazon is an Equal Opportunity Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.


· PhD in machine learning, operations research, applied mathematics or a related field
· Experience building predictive and optimization models
· Expert knowledge in at least one of following fields: machine learning, operations research, text mining and feature engineering
· Fluency in a high-level modeling language such as R, Python, Matlab or other statistical software
· Strong communication, influencing and partnership skills
· Ability to navigate conflicting priorities and ambiguous problem space(s)


· Ability to convey rigorous mathematical concepts and considerations to business and product teams
· Curiosity and desire to learn
· Experience in feature engineering and building advanced forecasting models
· Familiarity/experience with retail industry and the assortment planning space