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AbeBooks

Applied Scientist

Location
Vancouver, BC
Details
Full Time
2 days ago
The Economic Technology team (ET) is looking for an Applied Scientist to join our team in building Reinforcement Learning solutions at scale. The ET applies 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 day one 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 business and industry context to science and technology decisions. 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, acquiring expertise as needed. You decompose complex problems into straightforward solutions.

BASIC QUALIFICATIONS

• 4+ years of combined academic and research experience
• PhD in Machine Learning, Computer Science, Statistics, Operations Research, or related field
• 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 develop in Spark and Scala
• Ability to deliver under tight deadlines.

PREFERRED QUALIFICATIONS

• Deep knowledge of probabilistic machine learning
• Experience developing software in traditional programming languages (C++, Java, etc..).
• Ability to clean, transform, and merge your own data in a procedural language like Python or R
• Strong publication record in top-tier journals and conferences.
• Demonstrated ability to serve as a technical lead
Category
Engineering Information Technology