Lead Data Scientist, Operations Research

The Lead Data Scientist, Operations Research leads optimization, forecasting and decision modeling for Sensata’s operations, starting with Supply Chain and extending to Procurement and Engineering. The role turns complex problems in inventory, planning, scheduling and cost into production models that change how planners, buyers and engineers make decisions. It also sets the data science standards for operations AI use cases. The role also supports enterprise data science needs as priorities arise.

• Oversees developing innovative product solutions to continuously drive value for the customer
• Leads development project teams by defining data needs
• Advises on insights to customer challenges
• Identify valuable data sources and efficient collection processes
• Analyze large data sets to discover trends and value driven insights
• Leads predictive models and algorithms
• Oversees development processes and tools to analyze model performance and data accuracy
• Communicate complex findings and ideas using data visualization techniques

• A university degree required (i.e. Bachelors degree) or equivalent relevant work experience.
• Must be a team player able to work in a fast-paced environment with demonstrated ability to handle multiple competing tasks and demands
• Strong communication skills; oral, written and presentation
• Strong organization, planning and time management skills to achieve results
• Strong personal and professional ethical values and integrity
• Holds self-accountable to achieving goals and standards
• Proficient in Microsoft Office programs (Outlook, Word, PowerPoint, and Excel)
• Strong interpersonal & collaboration skills to work effectively with all levels of the organization including suppliers and/or external customers

• Lead data science modeling and approach for variety operations use cases, incl. Supply Chain, from framing the problem through production and adoption.
• Build optimization models for inventory policy, multi-echelon safety stock, item segmentation, capacity and production planning, and lab/test scheduling.
• Build demand forecasting and signal-reliability models for noisy customer and EDI demand.
• Develop should-cost and spend analytics to support Procurement decisions.
• Design empirical tests to evaluate vendor and planning-platform models, and hold vendors to agreed performance thresholds.
• Set standards for modeling, validation and backtesting. Define retraining and monitoring requirements with the AI Platform/MLOps team.
• Work directly with planners, buyers and business leaders so models are adopted and improve inventory, service and cost.
• Give technical direction and mentoring to data scientists and AI engineers working on operations use cases.

• 7+ years building optimization, ML and statistical models for operational problems, with models deployed and in sustained use.
• Deep operations research expertise: linear and mixed-integer programming, constrained optimization, inventory theory, scheduling and simulation.
• Hands-on experience with optimization solvers and frameworks (e.g., Gurobi, CPLEX, OR-Tools, Pyomo).
• Strong time-series forecasting skills, including hierarchical and probabilistic methods and rigorous backtesting.
• Strong Python and SQL; comfortable working with raw ERP, EDI and manufacturing data.
• Experience in manufacturing, automotive, industrial or distribution supply chains strongly preferred.
• Able to challenge and influence senior leaders and vendors with evidence.
• Master’s or PhD in Operations Research, Industrial Engineering, Statistics, Applied Mathematics or a related field preferred.

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Base Salary Range:

$ - $

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