We are currently looking for a motivated leader that has solid industrial research experience and management skills in inventory planning, supply chain management or relevant areas. He or she will lead the research scientist team in IPC (Buying, Removals, Assortment Optimization) where we are responsible for developing solutions to better manage/optimize worldwide inventory, while providing the best experience to our customers at the lowest possible cost. The research team is responsible for quantitative data analysis, building models and prototypes for inventory and supply chain systems, building state-of-the-art optimization algorithms. This team plays a significant role in the innovation pipeline from business needs, research work, prototyping/simulation, to implementation by working closely with a variety of colleagues in engineering, product management, operations, retail, finance, etc.
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Technical Responsibilities:
- Interact with engineering, operations and business teams to develop an understanding and domain knowledge of operational processes, system structures, and business requirements.
- Apply domain knowledge and business judgment to identify opportunities and quantify impact
- Come up with strategies with quantitative modeling and mathematical optimization to enhance existing and develop new inventory planning algorithms and strategies
- Apply advanced mathematical and heuristic optimization techniques to design scalable, optimal or near-optimal solutions
- Create prototypes to test devised solutions and leverage in-house simulation platforms for high-fidelity validation
- Work closely with engineers to integrate the prototypes into production systems using standard software development tools and methodologies
- Leverage policy evaluation platform to track the actual performance of a devised solution in production systems, identify areas with potential for improvement and work with internal teams to improve the solution with new features
Leadership Responsibilities:
- Mentor research scientist team members for their career development and growth
- Be data-driven, details driven and frequently audit the quality and scalability of solutions during the research process
- Be efficient in aligning research direction to business requirement and make the right judgment on research project prioritization
- Effectively advocate technical solutions to business stakeholders, engineering teams, as well as executive level decision makers
- Excel in evaluating the skill sets of candidates to make successful hiring decisions to meet business needs.
Basic Qualifications
M.S. in Operations Research, Computer Science, Applied Mathematics, or a closely related field. More than 10 years of industrial/academic experience in supply chain management, inventory management, large scale simulation, and mathematical modeling.
- Ability to manage and quantify improvement in multiple business areas resulting from business analytics, optimization techniques, and/or statistical modeling.
- Demonstrated use of modeling and optimization techniques tailored to meet business needs and proven achievements in industrial production systems.
- Strong leadership of experienced scientists as well as a successful record of developing junior members from academia/industry to a successful career track in a business environment
- Excellent written and verbal communication skills
Preferred Qualifications
Ph.D. in Operations Research, Computer Science, Applied Mathematics, or a closely related field. More than 10 years of industrial/academic experience in supply chain management, inventory management, large scale simulation, and mathematical modeling.
- Demonstrated experience to manage an industrial research team for three years or more.
- Familiarity with inventory planning concepts - forecasting, planning, optimization, and logistics - gained through work experience or graduate level education
- Expertise in one of the relevant areas: probability theory, queuing theory, game theory, simulation, decision analysis, stochastic models, system dynamics, and predictive modeling
- Experience with data engineers or business analysts in Big Data analytics
- A good understanding of analysis of algorithms and computational complexity
- Expertise in prototyping and scripting languages (R, Perl or Python) with applications of efficient large-scale data analysis in a complicated system
- Excellent written and verbal communication skills
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