Operations Research Principal Data Scientist

Zimmer, Inc.•Remote, Remote

1d•$140,000 - $175,000

About The Position

Drives advanced analytics and optimization across our supply chain and operational ecosystem. Bridging advanced machine learning, optimization, and simulation with real-world operational execution, delivering measurable impact across inventory, service levels, working capital, and patient care.

Requirements

Minimum Qualification: Bachelor's Degree and 6 years of relevant experience, or Associate's Degree and 8 years of relevant experience, or High School Diploma or Equivalent and 10 years of relevant experience
Expert proficiency in Python (ML, optimization libraries, automation) and/or R (statistical modeling) plus strong SQL skills for extracting, transforming, and analyzing large datasets from ERP, WMS, and clinical systems.
Optimization algorithms (linear programming, heuristics, simulation-based optimization).
Risk modeling for supply variability and disruption scenarios.
Experience working in cloud environments (Snowflake) for scalable analytics - familiarity with distributed data processing and cloud-native analytics patterns.
Proficiency in Power BI, Matplotlib, Seaborn, or similar tools.
Strong adherence to production-grade development practices: Modular, maintainable code; Version control (Git); Unit testing and documentation
Ability to clearly communicate complex insights to clinicians, operators, and executives.

Nice To Haves

Background in Healthcare Management Consulting with experience advising on operations, supply chain, or performance improvement.
Demonstrated ability to operate across industries and functional domains.
Experience working in regulated healthcare or medical device environments strongly preferred.
Proven track record of moving analytics from concept to operational execution.
Experience deploying models into operational systems.
Experience in demand forecasting and predictive analytics across multi-stage supply chains.

Responsibilities

Establishes reliable, analytics-ready datasets across the end-to-end supply chain and clinical usage data; partners with data engineering teams to ensure scalable, governed data pipelines.
Designs, builds, and deploys global Multi-Echelon Inventory Optimization (MEIO) models that balance service levels, cost, and risk.
Develops and operationalizes demand forecasting models that account for variability, seasonality, market dynamics, and clinical drivers.
Applies optimization techniques (linear programming, mixed-integer programming, heuristics) to inventory positioning and replenishment decisions.
Analyzes usage patterns of consigned and loaned medical devices across sites and procedures; develops analytics to recommend when to shift between consignment and ownership models.
Reduces excess inventory, free working capital, and improves asset utilization without impacting patient care.
Builds risk and disruption models to assess exposure to demand volatility, supplier constraints, and geopolitical or regional risks.
Leverages simulation and what-if analysis to test inventory and supply strategies prior to deployment.
Supports supply chain resilience planning and contingency strategies.
Establishes feedback loops to continuously refine forecasts, optimization logic, and assumptions.
Uses analytical insights to streamline workflows and automate replenishment and decision processes.
Partners closely with supply chain planners, procurement, logistics, finance, and field teams.
Translates analytical outputs into clear, actionable recommendations for non-technical stakeholders; drives adoption of data-driven decision making across operational teams.
Defines and tracks KPIs such as inventory turns, service levels, stockout rates, cost savings, and working capital improvements.
Quantifies impact on operational efficiency, financial performance, and patient outcomes; communicates results to executive and operational leadership.