Sr. Data Scientist, Ops Research

McKesson•Irving, TX

2d•$136,300 - $227,100

About The Position

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

Degree or equivalent and typically requires 7+ years of relevant experience
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
Candidate must be authorized to work in the U.S, now or in the future, without the support from McKesson.

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.