Job Description Summary

The Sr Data Scientist will work in teams addressing statistical, machine learning and data understanding problems in a commercial technology and consultancy development environment. In this role, you will contribute to the development and deployment of modern machine learning, operational research, semantic analysis, and statistical methods for finding structure in large data sets.

Job Description

Role Overview

GE HealthCare is advancing enterprise-scale transformation through strategic Big Bets in Supply Chain, Inventory Management, and Manufacturing Planning. The Operations Research Center of Excellence (OR COE), within the Chief Data & Analytics Office, is responsible for building mathematically rigorous, production-grade decision systems that directly influence planning, policy setting, and operational execution.

As a Sr. Operations Research Data Scientist (LPB2), you will be a senior individual contributor within the OR COE, with a primary focus on inventory optimization, safety stock policy design, and simulation-driven decision support. This role is inventory-first and optimization-led, with direct contribution to GEHC’s emerging supply chain digital twin capabilities.

This position aligns with GEHC LPB2 expectations: independent execution, strong technical depth, and the ability to translate complex optimization models into business-ready decisions.

Core Responsibilities

Inventory Optimization & Safety Stock Modeling

• Design, build, and maintain inventory optimization models supporting safety stock optimization, service-level targeting, replenishment policies, and working capital trade-offs.
• Develop and apply multi-echelon inventory models incorporating demand variability, lead time uncertainty, and service constraints.
• Quantify cost-to-serve, service risk, and inventory exposure through optimization and simulation.

Simulation & Digital Twin Enablement

• Build discrete-event and Monte Carlo simulation models to evaluate inventory policies, network design choices, and operating scenarios.
• Contribute core optimization and simulation logic to GEHC’s supply chain digital twin, enabling scenario-based planning and decision experimentation.
• Support what-if analysis, stress testing, and policy evaluation across demand, supply, and network disruptions.

Optimization Model Development

• Formulate and solve large-scale linear, mixed-integer, and stochastic optimization problems using commercial solvers.
• Diagnose infeasibility, convergence, and performance issues in enterprise-scale models.
• Balance model fidelity with tractability to support production deployment and repeatable decision use.

OR COE Contribution

• Act as a core technical contributor within the Operations Research COE, helping define modeling standards, reusable inventory components, and validation practices.
• Contribute to model documentation, explainability artifacts, and governance processes required for operational and executive adoption.

Cross-Functional Delivery

• Partner with inventory planning, supply chain, manufacturing, and finance stakeholders to ensure models reflect operational reality.
• Translate business questions into mathematically precise problem formulations with clear objectives, constraints, and assumptions.

Business Impact & Communication

• Translate optimization outputs into clear recommendations, policy guidance, and trade-off narratives.
• Communicate model logic, assumptions, and limitations clearly to non-technical stakeholders and senior leaders.

Experience & Qualifications

Required Experience

• Demonstrated hands-on experience delivering inventory optimization or safety stock models in a production or decision-support context.
• Experience solving supply chain, inventory, or planning problems using formal optimization or simulation techniques.

Education

• Master’s or PhD in Operations Research, Industrial Engineering, Applied Mathematics, Systems Engineering, or a closely related quantitative discipline.

Core Technical Skills (Required)

• Strong foundation in inventory theory, optimization, and simulation.
• Hands-on experience with commercial solvers such as Gurobi, CPLEX, and/or Seeker.
• Proficiency in Python for model development, experimentation, and integration.
• Experience formulating LP/MIP models and troubleshooting performance and scalability issues.

Preferred / Differentiating Skills

• Experience with multi-echelon safety stock optimization and service-level constrained planning.
• Familiarity with stochastic demand modeling and lead time variability.
• Experience contributing optimization models to planning systems, analytics platforms, or digital twin architectures.

Professional Skills

• Ability to independently execute complex analytical work with minimal oversight, consistent with LPB2 expectations.
• Strong problem-framing skills and sound judgment in balancing rigor with practicality.
• Clear written and verbal communication, particularly around trade-offs and decision implications.

Additional Information