Applied Scientist, Selection and Inventory Science will work on high-impact, real-world problems in product selection and inventory management using 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 and partner closely with engineering and business teams to translate models into trusted, scalable decisions. You will publish your work in top conferences and journals. Key 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 for stakeholders. Basic qualifications: PhD, or Master's degree plus 4+ years of CS, CE, ML or related field experience; 3+ years of building models for business applications; publications or patents at top-tier conferences or journals; proficiency in Java, C++, Python or related languages; experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Preferred qualifications: experience in state-of-the-art deep learning model architectures and training and optimization, including model pruning; experience in designing experiments and statistical analysis of results; experience in professional softwa

Applied Scientist, Selection and Inventory Science will work on high-impact, real-world problems in product selection and inventory management using 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 and partner closely with engineering and business teams to translate models into trusted, scalable decisions. You will publish your work in top conferences and journals. Key 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 for stakeholders. Basic qualifications: PhD, or Master's degree plus 4+ years of CS, CE, ML or related field experience; 3+ years of building models for business applications; publications or patents at top-tier conferences or journals; proficiency in Java, C++, Python or related languages; experience in algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing. Preferred qualifications: experience in state-of-the-art deep learning model architectures and training and optimization, including model pruning; experience in designing experiments and statistical analysis of results; experience in professional softwa