Senior Operations Research Scientist

About the role

About The Company

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.

About The Role

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.

Qualifications

Degree in Operations Research, Data Science, Statistics, Applied Mathematics, or related field

Typically 7+ years of relevant experience in analytics, operations research, or supply chain modeling

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

Proficiency in data wrangling using SQL

Experience with statistical modeling in Python and/or R

Excellent communication skills to convey technical results to both technical and non-technical audiences

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

Translate simulation and optimization outputs into actionable recommendations for business partners

Design, implement, and validate stochastic process simulation models to optimize supply chain operations

Collaborate with cross-functional teams to identify opportunities for analytic solutions and process improvements

Drive the adoption of innovative modeling approaches and measure their impact on business outcomes

Maintain and enhance existing models and solutions to ensure continuous improvement

Present findings and insights to senior leadership and stakeholders to influence strategic decision-making

Stay current with emerging analytic techniques and incorporate them into ongoing projects

Benefits

Competitive salary aligned with experience and skills

Comprehensive health, dental, and vision insurance plans

Retirement savings plans with company match

Paid time off and holidays

Professional development opportunities and tuition reimbursement

Flexible work arrangements and work-life balance initiatives

Employee wellness programs and resources

Equal Opportunity

McKesson is an Equal Opportunity Employer. We provide equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. We are committed to creating an inclusive environment where all individuals can thrive. For additional information on our full Equal Employment Opportunity policies, please visit our Equal Employment Opportunity page.