Applied Scientist, Selection And Inventory Science

Description

• As an Applied Scientist, you will work on high-impact, real-world problems in product selection and inventory management using techniques from operations research, causal inference, forecasting, reinforcement

Description

• As an Applied Scientist, you will work on high-impact, real-world problems in product selection and inventory management using techniques from operations research, causal inference, forecasting, reinforcement learning and agentic artificial intelligence. You will develop state-of-the-art solutions to optimize customer experience with supply chain levers. You will partner closely with engineering and business teams to turn these models into trusted, scalable decisions. You will publish your work in top conferences and journals. Key job responsibilities - Develop stochastic and multi-objective optimization models for assortment planning. - Apply causal inference and forecasting to measure impact and inform decisions. - Build reinforcement learning foundation models for large-scale sequential decision making in inventory management. - Translate rigorous science into clear, actionable recommendations, and communicate them to stakeholders.

Basic Qualifications

• - PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience - 3+ years of building models for business application experience - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience programming in Java, C++, Python or related language - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications