RWE Data Scientist
About The Position
As a Real-World Evidence Data Science intern at Stryker, you will work cross-functionally with different departments including Clinical Affairs, Health Economics & Outcomes Research (HEOR), Regulatory Affairs, and Marketing to support real-world evidence generation programs. You will assist in the design and execution of observational studies using claims (e.g., Premier PINC AI, NIS) and other real-world data sources. The role involves supporting data extraction, cleaning, and analysis of structured and unstructured healthcare data, including applying NLP techniques to unstructured billing/clinical data. You will prepare literature review summaries and evidence syntheses to support publication and regulatory submission efforts, and build and refine statistical models (e.g., propensity matching, survival analysis) under the mentorship of the RWE Research team. Additionally, you will shadow cross-functional team meetings to gain exposure to how RWE informs regulatory, reimbursement, and commercial strategy.
Requirements
- Currently pursuing a Master's degree in Biostatistics, Epidemiology, Health Services Research, Health Economics and Outcomes Research (HEOR), Health/Biomedical Informatics, Data Science, or a related quantitative field; must remain enrolled in a degree-seeking program after the internship
- Cumulative 3.0 GPA or above (verified at time of hire)
- Must be legally authorized to work in the U.S. and not require employment-based sponsorship now or in the future
- Proficiency in SQL and at least one statistical/analytical programming language (Python or R)
- Coursework or applied project experience with observational/real-world data (claims, EHR, or registry data)
- Strong written and verbal communication skills, with proven ability to collaborate and build relationships
- Demonstrated leadership, problem-solving, and organizational skills with the ability to manage multiple priorities
- Proficiency in Microsoft Office (Excel, Word, PowerPoint) and eagerness to learn in a dynamic environment
Nice To Haves
- Prior exposure to claims databases (Medicare, MarketScan, Premier PINC AI, Optum) or EHR data structures
- Familiarity with causal inference methods (propensity score matching, instrumental variables) and/or survival analysis
- Experience with NLP applied to unstructured healthcare text
- Prior coursework, thesis, or practicum work in a medtech, pharma, or payer setting
Responsibilities
- Work cross functionally with different departments including Clinical Affairs, Health Economics & Outcomes Research (HEOR), Regulatory Affairs, and Marketing to support real-world evidence generation programs
- Assist in the design and execution of observational studies using claims (e.g., Premier PINC AI, NIS) and other real-world data sources
- Support data extraction, cleaning, and analysis of structured and unstructured healthcare data, including applying NLP techniques to unstructured billing/clinical data
- Prepare literature review summaries and evidence syntheses to support publication and regulatory submission efforts
- Build and refine statistical models (e.g., propensity matching, survival analysis) under the mentorship of the RWE Research team
- Shadow cross-functional team meetings to gain exposure to how RWE informs regulatory, reimbursement, and commercial strategy
Benefits
- $20 min hourly wage – $35 max hourly wage
- sign-on bonus
- 11 paid holidays annually
- paid corporate housing or a living stipend, dependent upon hiring location
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What This Job Offers
Career Level
Intern
