Sr. Applied Scientist

Job ID: 1309363 | Services LLC


Amazon aims to exceed the expectations of our customers by ensuring that their orders, no matter how large or small, are delivered as quickly, accurately, and cost effectively as possible. To meet this goal, Amazon has invested in Amazon Logistics, a world class last mile operation. We are looking for a dynamic, resourceful, and organized Sr. Applied Scientist within Amazon Logistics’ Amazon Flex organization to develop the right data-driven solutions to support the most critical components of this rapidly scaling operation.
As part of the Driver Science team, you’ll partner closely with Science, Product, and Program teams to design of new and innovative solutions to organizational and business problems, using statistical best practices to justify design decisions using data. You will be expected to be a subject matter expert in quantitative analysis and to communicate highly complex findings to multiple audiences. You may come from one of many different types of quantitative backgrounds, but you must be an expert in big data, machine learning, and productionalization. You will be working closely with Tech teams but will be responsible to helping rapidly push models into production and measuring business impact. You are a thought leader, creating scientific strategies and visions to solve ambiguous problems.

Key Responsibilities
· Execute global research initiatives
· Conduct, direct, and coordinate all phases of research projects, demonstrating skill in all stages of the analysis process, including defining key research questions, recommending measures, working with multiple data sources, evaluating methodology and design, executing analysis plans, interpreting and communicating results
· Share deep knowledge in machine learning to our problem space.
· Work in an ambiguous environment


· PhD or Master’s degree in Machine Learning, Computer Science, Computer Engineering, Statistics, Applied Mathematics, or a related field
· Experience building machine learning models
· Fluency in a high-level modeling language such as Python or other statistical software
· Strong communication, influencing and partnership skills


· A natural curiosity and desire to learn
· Ability to convey rigorous mathematical concepts and considerations to non-experts
· Ability to distill problem definitions, models, and constraints from informal business requirements
· Ability to deal with ambiguity and competing objectives
· Experience with big data and analytics
· Experience designing and supporting large-scale distributed systems in a production environment
· Knowledge of machine learning applications in predictive and prescriptive modeling, personalization, recommendation, pricing, fraud detection and prevention.
· Proficiency in at least one modern programming language such as Java