Data Scientist, Operations Research — Supply Chain

Cardinal Health; United States; On-site; USD 95,000 - 122,000; Full time

Benefits offered by this job

Medical, dental and vision coverage

Paid time off

HSA (Health Savings Account)

401k savings plan

Work-Life resources

Paid parental leave

Job summary

Cardinal Health is seeking a Data Scientist, Operations Research Analyst to join the DS2 team focused on improving pharmaceutical supply chain performance. You will develop predictive and prescriptive models, work cross-functionally, and translate analyses into actionable recommendations.

The role emphasizes capacity planning, distribution design, inventory strategies, and collaboration with Data Engineers. Travel up to 5% may be required.

Qualifications

• 2–4 years of experience in data science/OR; quantitative degree preferred.
• Experience applying DS/OR to logistics and supply chain problems.
• Proficiency with SQL and databases; strong communication skills.

Responsibilities

• Listen to clients and clearly define business problems.
• Perform exploratory analyses to identify root causes and test hypotheses.
• Develop predictive models to forecast volumes and capacity needs.
• Create segmentation models to support service differentiation.
• Build optimization and simulation models for capacity decisions.
• Develop prescriptive models using external tools and custom code.
• Summarize financial and operational impacts of recommendations.
• Collaborate with Data Engineers to design data models for analyses.

Skills

SQL

Python

R

Tableau

Alteryx

SAS

Statistics

Education

Bachelor's degree in a quantitative field

MS/MBA in Data Science or OR

Tools

AnyLogic

CPLEX

Tableau

Alteryx

SAS

Job description

Cardinal Health is seeking a Data Scientist, Operations Research Analyst to join the DS2 team focused on improving pharmaceutical supply chain performance. You will develop predictive and prescriptive models, work cross-functionally, and translate analyses into actionable recommendations.

The role emphasizes capacity planning, distribution design, inventory strategies, and collaboration with Data Engineers. Travel up to 5% may be required.