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Health Economics and Outcomes Summer Intern
arine • Remote
Posted: April 17, 2026
Job Description
The Role:
We are looking for a Health Economics and Outcomes Intern to join our HEOR Team for the summer. This role is designed for a current graduate student, recent graduate, or someone with equivalent early-career experience who is passionate about using advanced statistical methods and AI tools to improve population health and reduce the total cost of care through medication optimization at health-plan scale.
Working closely with our Senior Researchers and Data Scientists, you will apply causal inference frameworks to real-world healthcare data to help us understand the "why" behind clinical and economic outcomes. You’ll gain hands-on experience with health care claims data, apply causal inference methods to real-world scenarios, and receive mentorship from an experienced team of researchers and data scientists.
What You’ll Be Doing (Learning & Contributing):
- Support Causal Analysis: Assist in designing and conducting analyses using causal inference methods to evaluate program impact on medication adherence, costs, and utilization.
- AI-Augmented Development: Utilize AI tools to accelerate and improve causal analysis code infrastructure. For example, work with researchers and data scientists to improve team efficiency and robustness of results by integrating agentic AI coding tools into the analysis process.
Who You Are and What You Bring:
- Current Graduate Student: Currently enrolled in (or recently graduated from) a Master’s or PhD program, or someone with equivalent early-career experience in Statistics, Economics, Public Health, Epidemiology, Health Informatics, or a related quantitative field.
- Causal Inference Toolkit: Understanding and practical experience with causal inference methods (ex. propensity score matching, difference-in-differences, and/or regression discontinuity).
- AI for Coding: Proficiency or interest in using AI tools to assist in writing SQL, Python, or R scripts for cleaning data and conducting statistical analyses.
- Proactive Problem Solver: A self-starter who is comfortable working independently in a fast-paced, high-growth environment.
Nice to Have:
- Experience with health care claims and administrative data
- Machine learning causal inference experience (ex. causal forests)
Requirements:
- Must live in and be eligible to work in the United States.
- Stable high-speed internet connection and a private work area for HIPAA compliance.
- Ability to pass a background check and complete Information Security/HIPAA training upon hire.
Compensation Range: $25-30/hour.
#LI- Remote
Additional Content
The Role:
We are looking for a Health Economics and Outcomes Intern to join our HEOR Team for the summer. This role is designed for a current graduate student, recent graduate, or someone with equivalent early-career experience who is passionate about using advanced statistical methods and AI tools to improve population health and reduce the total cost of care through medication optimization at health-plan scale.
Working closely with our Senior Researchers and Data Scientists, you will apply causal inference frameworks to real-world healthcare data to help us understand the "why" behind clinical and economic outcomes. You’ll gain hands-on experience with health care claims data, apply causal inference methods to real-world scenarios, and receive mentorship from an experienced team of researchers and data scientists.
What You’ll Be Doing (Learning & Contributing):
- Support Causal Analysis: Assist in designing and conducting analyses using causal inference methods to evaluate program impact on medication adherence, costs, and utilization.
- AI-Augmented Development: Utilize AI tools to accelerate and improve causal analysis code infrastructure. For example, work with researchers and data scientists to improve team efficiency and robustness of results by integrating agentic AI coding tools into the analysis process.
Who You Are and What You Bring:
- Current Graduate Student: Currently enrolled in (or recently graduated from) a Master’s or PhD program, or someone with equivalent early-career experience in Statistics, Economics, Public Health, Epidemiology, Health Informatics, or a related quantitative field.
- Causal Inference Toolkit: Understanding and practical experience with causal inference methods (ex. propensity score matching, difference-in-differences, and/or regression discontinuity).
- AI for Coding: Proficiency or interest in using AI tools to assist in writing SQL, Python, or R scripts for cleaning data and conducting statistical analyses.
- Proactive Problem Solver: A self-starter who is comfortable working independently in a fast-paced, high-growth environment.
Nice to Have:
- Experience with health care claims and administrative data
- Machine learning causal inference experience (ex. causal forests)
Requirements:
- Must live in and be eligible to work in the United States.
- Stable high-speed internet connection and a private work area for HIPAA compliance.
- Ability to pass a background check and complete Information Security/HIPAA training upon hire.
Compensation Range: $25-30/hour.
#LI- Remote