Lead Data Scientist – Supply Chain

McKesson • Irving, TX

1d

About The Position

The Lead Data Scientist, Supply Chain - Operations Research role is responsible for architecting and implementing AI/ML products to enhance the efficiency and effectiveness of McKesson’s supply chain operations as part of a McKesson’s Supply Chain & Operations COE. Our team applies data science methodologies to interdisciplinary business problems across Operations & Supply Chain. This position will work on strategic in-flight use cases around inventory and working capital management. The position’s objectives are: Develop stochastic models to facilitate next best actions across Supply Chain Architect and lead implementation of AI/ML driven operation research frameworks to optimize enterprise inventory management systems Lead development of enterprise-scale digital twin for inventory management The candidate should possess the ability to perform statistical modelling techniques and derive business insights that are required to drive analytic innovation at McKesson. The candidate should also be an active learner able to grasp and apply new analytic approaches, as well as mentor junior / developing resources. Position Description The purpose of this position is to architect, implement, drive adoption, and measure impact of innovative analytic solutions at McKesson, as well as make significant improvements to existing solutions.

Requirements

• Experience: 7+ years data science / analytics / programming experience based on combination of industry and academic experience
• Education: bachelor’s degree in a technical field such as: Operations Research, Computer Science, Statistics, Applied Mathematics, Engineering or related quantitative / STEM majors. Masters and/or PhD preferred.
• Experience with one or more of optimization toolkits/libraries like CPlex, Gurobi, XPressMP, Open-source solvers (CBC, GLPK) etc
• Deep knowledge of statistical methods, advanced modeling techniques, along with optimization & OR techniques
• Demonstrated experience with solving enterprise inventory optimization and/or transportation optimization problems
• Ability to communicate your results from deeply technical to non-technical audiences
• Demonstrated ability to tackle problems across the full data stack, from data wrangling (leveraging SQL or other methodologies) to stakeholder consumption at scale
• Deep knowledge of machine learning / data science best practices
• Knowledge of statistical programming (SAS, R, MATLAB)
• Ability to communicate technical concepts to non-technical audiences
• Demonstrated experience with objected oriented programming (Python, Java, C#, VBA, etc.)
• Strong grasp of fundamental statistical concepts: linear regression, A/B testing, outlier analysis, probability distributions, tests for independence, etc.
• Analysis/Process Thinking
• Team player
• Strong verbal and written communication
• Knowledge of relational databases (e.g. MS SQL Server, Snowflake, Oracle)
• Proficient with Excel spreadsheets, financial modeling, and reporting
• Prior data mining experience using enterprise systems (SAP or JD Edwards preferred)
• Knowledge of data warehousing & ETL best practices is a plus

Nice To Haves

• Knowledge of cloud computing platforms is a plus (e.g. Azure, AWS, Google Cloud, Databricks)

Responsibilities

• Develop inventory optimization / multi-echelon simulation framework for supply chain
• Lead in development of statistical simulation decision frameworks
• Develop of AI/ML driven continuous monitoring systems in order to dynamically track McKesson network and continuously identify areas of working capital opportunity
• Play a leading role in adding Reinforcement Learning to set dynamic prices & safety stocks
• Support stakeholders’ analytic needs, gather user requirements, help drive adoption
• Cultivate business development opportunities
• Assist in developing and maintaining long-term stakeholder relationships and networks

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level