- PhD in Machine Learning, Computer Science, Statistics, Operations Research, or related field
- 5+ years of industry experience
- Excellent communication, writing and presentation skills
- Experience in designing analytic and/or algorithmic solutions to business or operational problems
- Significant hands-on experience with at least two programming languages.
- Ability to deliver under tight deadlines.
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.
Experience building large-scale machine-learning models
- Experience with Big Data technologies such as AWS, Hadoop, Spark
- Experience building complex software systems that have been successfully delivered to customers
- Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
- Experience with machine learning, data mining, and/or statistical analysis tools
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