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- PhD, or Master's degree and 4+ years of quantitative field research experience
- Experience investigating the feasibility of applying scientific principles and concepts to business problems and products
- Ph.D. in Operations Research, Machine Learning, Statistics, Computer Science, Applied Mathematics, Operations Management, Industrial Engineering, Economics or a related field.
- 2+ years of experience in solving complex problems in the area of operations research, statistics or machine learning, as well as in developing strategies for large-scale supply chain, transportation and logistics networks.
- Ability to perform quantitative, economic, and numerical analyses of large-scale systems under uncertainty using statistical and optimization tools such as Python, R and XPRESS to determine solution strategies for complex decision-making problems.
- Fluency in using mathematical optimization or machine learning to develop solution methods to be used by in-house decision support tools and software.
- Proficiency working with Python, SQL and/or C/C++.
- Excellent written and verbal communication skills while addressing both technical and business people, ability to speak at a level appropriate for the audience, and skills to present business cases, and document models, analyses, and results to influence important decisions.
- 2+ years of hands-on experience in using models to solve complex real-world business problems.
- Experience in supply chains, transportation and logistics models.
- Significant peer-reviewed scientific contributions in premier journals and conferences.
- Programming experience using at least one modern programming language such as Python, Java, or C++.
- Proficiency in development, validation and implementation optimization or machine learning models for large-scale applications.
- Superior verbal and written communication and presentation skills, ability to convey rigorous mathematical concepts and considerations to non-experts.
- Proven ability to work effectively in a cross-functional team.
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