The Economic Technology team (EconTech) is looking for an Applied Scientist to build Reinforcement Learning solutions to solve economic problems at scale. EconTech uses Machine Learning, Reinforcement Learning, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business. We also develop statistical models and algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Economists, Engineers, and Scientists incubating and building disruptive solutions using cutting-edge technology to solve some of the toughest business problems at Amazon.
You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable distributed services. You will partner with scientists, economists, and engineers to help invent and implement scalable ML, RL, and econometric models while building tools to help our customers gain and apply insights. This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale economic problems, enable measurable actions on the Consumer economy, and work closely with scientists and economists. We are particularly interested in candidates with experience building predictive models and working with distributed systems.
As an Applied Scientist, you bring structure to ambiguous business problems and use science, logic, and practical experience to decompose them into straightforward, scalable solutions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems; you're interested in learning; and you acquire skills and expertise as needed.
· An MS in Computer Science, Mathematics, Statistics or another highly quantitative field
· 5+ years of hands-on experience in predictive modeling, analysis and machine learning
· 5+ years of experience in using R, Python, Matlab or similar languages
· 2+ years of experience with Java, C++, or similar languages
· Proficiency in model development, model validation and model implementation for large-scale applications
· Ability to convey mathematical results to non-science stakeholders
· Strength in clarifying and formalizing complex problems
· Excellent communication, writing and presentation skills
· Ability to deliver under tight deadlines
· PhD in Machine Learning, Computer Science, Statistics, Operations Research, or related field
· Extensive experience applying theoretical models in an applied environment.
· Experience building large-scale machine-learning models
· Strong CS fundamentals in data structures, problem solving, algorithm design and complexity analysis;
· Experience with defining research and development practices in an applied environment;
· Experience working with Deep Learning frameworks (MxNet, TensorFlow, etc.);
· Proven track record in technically leading and mentoring scientists;
· Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.