Applied Scientist, Analytics Technology and Engineering
3 weeks ago
About the Role
We are seeking an Applied Scientist to join our Analytics Technology and Engineering team in China. As an Applied Scientist, you will be responsible for developing strategic models and automation tools that improve the economics of Amazon's worldwide fulfillment networks.
Key Responsibilities
- Design and develop complex mathematical, simulation, and optimization models to drive business and technical solutions in areas such as vehicle routing, inventory management, network flow, and supply chain optimization.
- Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, dynamic programming, network flows, and algorithms to design optimal or near-optimal solution methodologies.
- Translate business questions and concerns into specific analytical questions that can be answered with available data using statistical and machine learning methods.
- Prototype models using modeling and programming languages with efficient data querying and modeling infrastructure.
- Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions.
- Collaborate with colleagues from multidisciplinary science, engineering, and business backgrounds.
- Manage your own process, prioritize and execute on high-impact projects, triage external requests, and ensure timely delivery.
Requirements
- Ph.D. or Master's degree in Operations Research, Industrial Engineering, Applied Mathematics, Machine Learning, Statistics, Computer Science, or a related quantitative field.
- Hands-on experience in Python, Java, C++, or other similar languages.
- Ability to convey mathematical results to non-science stakeholders.
- Strength in clarifying and formalizing complex problems.
- Superior verbal and written communication and presentation skills in both Chinese and English.
Preferred Qualifications
- Experience with large-scale optimization and decomposition techniques such as column generation and meta-heuristics.
- Practical experience applying ML to solve complex problems in an applied environment.
- Significant peer-reviewed scientific contributions in premier journals and conferences.
- Strong CS fundamentals in data structures, problem-solving, algorithm design, and complexity analysis.
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