Lead Data Scientist, Operations Research

Sensata•Attleboro, MA

About The Position

The Lead Data Scientist, Operations Research leads optimization, forecasting, and decision modeling for Sensata’s operations, beginning with Supply Chain and extending to Procurement and Engineering. This role translates complex challenges in inventory, planning, scheduling, and cost into production models that improve how planners, buyers, and engineers make decisions. It also establishes data science standards for operations AI use cases and supports enterprise data science needs as priorities arise. The role leads data science modeling for operational use cases from problem framing through production deployment and adoption. It builds optimization models, forecasting and signal-reliability models, and procurement analytics; evaluates vendor and planning-platform models; and defines standards for modeling, validation, backtesting, retraining, and monitoring. The Lead Data Scientist works directly with business stakeholders to ensure models are adopted and improve inventory, service, and cost, while providing technical direction and mentoring to data scientists and AI engineers.

Requirements

University degree, such as a bachelor's degree, or equivalent relevant work experience.
At least 7 years of experience building optimization, machine-learning, and statistical models for operational problems, with models deployed and in sustained use.
Deep operations research expertise, including linear and mixed-integer programming, constrained optimization, inventory theory, scheduling, and simulation.
Hands-on experience with optimization solvers and frameworks such as Gurobi, CPLEX, OR-Tools, or Pyomo.
Strong time-series forecasting skills, including hierarchical and probabilistic methods and rigorous backtesting.
Strong Python and SQL skills and comfort working with raw ERP, EDI, and manufacturing data.
Ability to work as a team player in a fast-paced environment and handle multiple competing tasks and demands.
Strong oral, written, and presentation communication skills.
Strong organization, planning, and time-management skills.
Strong personal and professional ethical values and integrity.
Accountability for achieving goals and standards.
Proficiency with Microsoft Office programs, including Outlook, Word, PowerPoint, and Excel.
Strong interpersonal and collaboration skills to work effectively with all organizational levels, suppliers, and/or external customers.
Ability to challenge and influence senior leaders and vendors with evidence.

Nice To Haves

Experience in manufacturing, automotive, industrial, or distribution supply chains.
Master's or PhD in Operations Research, Industrial Engineering, Statistics, Applied Mathematics, or a related field.

Responsibilities

Lead data science modeling and approaches for operational use cases, including Supply Chain, from problem framing through production and adoption.
Build optimization models for inventory policy, multi-echelon safety stock, item segmentation, capacity and production planning, and laboratory/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 assess them against 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 to drive model adoption and improve inventory, service, and cost.
Provide technical direction and mentoring to data scientists and AI engineers working on operations use cases.
Oversee development of innovative product solutions to continuously drive customer value.
Lead development project teams by defining data needs.
Advise on insights into customer challenges.
Identify valuable data sources and efficient collection processes.
Analyze large datasets to discover trends and value-driven insights.
Lead predictive models and algorithms.
Oversee development processes and tools for analyzing model performance and data accuracy.
Communicate complex findings and ideas using data visualization techniques.

Benefits

Medical insurance
Dental insurance
Vision insurance
Life insurance
Disability insurance
Education reimbursement
Wellness programs
401(k) retirement plan with company matching
Paid time off, including vacation or flex-time, sick, bereavement, and parental leave
Certain positions may be eligible for short-term and long-term incentive programs