Sr. Data Scientist, Ops Research
Azure Ml
Big Data
Cloud Platform
Cloud Platforms
Data Analysis
Data Analytics
Data Engineer
Data Platform
Data Processing
Data Science
Data Warehouse
Data Warehousing
Databases
Databricks
Engineering
Linear Programming
Manufacturing Systems
Mixed Integer Programming
Operations Research
Optimization
Platform Engineering
Simulation
SQL
Stochastic Modeling
Supply Chain Analytics
Job Description
Build stochastic process simulation and optimization products to improve supply chain operations within McKesson’s Enterprise Data Science team.
Responsibilities
- Develop and apply digital twins and simulation/optimization frameworks to support decisions across supply chain areas including inventory, transportation, and labor planning.
- Translate simulation and optimization outputs into actionable recommendations for business partners.
- Architect, implement, and drive adoption of stochastic process simulation and optimization solutions; measure impact and make significant improvements to existing solutions.
Requirements
- Demonstrated experience developing stochastic process simulations to inform business decisions in inventory, transportation, or other supply chain-related domains.
- Strong foundation in probability and statistics, including random variables, probability distributions, hypothesis testing, regression, and modern machine learning methods.
- Demonstrated experience in data wrangling using SQL.
- Experience with statistical modeling in Python and/or R.
- Ability to communicate results to technical leaders and non-technical executive audiences.
- Degree or equivalent, typically requiring 7+ years of relevant experience.
Tools & Technologies
- SQL, Python, R
- Optimization solvers: CPLEX, Gurobi, Xpress, CBC, GLPK
- Data and ML platforms: Databricks, Snowflake, Azure ML
Preferred Skills
- 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.
Location & Compensation
- Location: Irving, TX (onsite)
- Base pay range: USD 136,300 - 227,100 per year
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