Senior Data Scientist - Operations Research
About The Position
McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. The Senior Operations Research Scientist role is responsible for architecting and implementing simulation and optimization products to enhance the efficiency and effectiveness of McKesson’s supply chain operations as part of the Operations Research group within the Enterprise Data Science Team. Our team applies data science and operations research methodologies to interdisciplinary business problems across Supply Chain Operations. This position will work on strategic in-flight use cases around inventory optimization and upcoming use cases around transportation and network modelling. The candidate should possess the ability to develop statistical models and derive business insights that are required to drive innovation at McKesson. The candidate should also be an active learner able to grasp and apply new analytic approaches. Position Description The purpose of this position is to architect, implement, drive adoption, and measure the impact of innovative stochastic process simulation and optimization solutions at McKesson, as well as make significant improvements to existing solutions.
Requirements
- Experience: 5+ years operations research / data science / programming experience based on combination of industry and academic experience.
- Education: Masters or Ph.D. degree in a relevant technical field such as: Statistics, Operations Research, Applied Mathematics, Industrial Engineering or other related quantitative fields.
- Demonstrated experience in developing stochastic process simulations to guide business decisions across inventory, transportation, or other supply chain related fields
- Strong foundation in probability and statistics, including random variables, probability distributions, hypothesis testing, regression, and modern machine learning methods.
- Demonstrated experience in data wrangling problems leveraging SQL
- Experience with statistical modeling in Python and/or R
- Experience in communicating results to technical leaders and non-technical executive audiences
Nice To Haves
- Experience with commercial or open‑source optimization solvers (e.g., CPLEX, Gurobi, Xpress, CBC, GLPK).
- Familiarity with reinforcement learning or approximate dynamic programming techniques.
- Experience developing dashboards, applications, or decision‑support tools that expose model outputs to business users.
- Exposure to financial modeling, cost optimization, or pricing analytics.
- Experience working in modern data and ML platforms such as Databricks, Snowflake, and Azure ML.
Responsibilities
- Develop and apply digital twins and simulation/optimization frameworks to aid decision making across supply chain areas such as inventory, transportation, and labor planning.
- Ability to translate simulation and optimization outputs into concrete recommendations to business partners.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
501-1,000 employees
