HEOR Researcher

Arine is on a mission to improve patient outcomes and lower the cost of healthcare at scale. The Health Economics and Outcomes Research (HEOR) team turns large, real-world health datasets into evidence that improves medication use, sharpens which interventions we prioritize, and demonstrates the impact of our programs to health-plan clients across commercial, Medicare Advantage, and Medicaid Managed Care lines of business.

We are looking for a well-rounded HEOR Researcher with experience in medication adherence research. You will also analyze the broader portfolio of programs Arine runs, including interventions that aim to reduce total cost of care and acute care utilization for high-risk, high-cost members. Across this work, you will help answer important research questions: what causes non-adherence, what are the leading indicators of risk, and how effectively do Arine's interventions improve outcomes. You will quantify opportunities for improvement, evaluate the impact of our interventions, and help determine which clinical tasks Arine surfaces and prioritizes for members. This work directly supports quality (for example, CMS Star Ratings Part D adherence measures for diabetes, RAS antagonists, and statins) and total cost of care performance and feeds platform functionality and executive-level deliverables for our clients.

This is a dynamic, highly cross-functional role and a great opportunity to help shape a fast-growing company from the ground up. You will report to the Director of HEOR and partner closely with clinicians, data scientists, operations leads, and client stakeholders.

What You’ll Be Doing:

• Research drivers of medication adherence: Design and independently conduct studies of what drives adherence and non-adherence using pharmacy and medical claims, enrollment, and supplemental data. These studies span clinical comorbidities (including behavioral health), social determinants of health (such as social vulnerability), demographics, utilization (such as inpatient and skilled-nursing stays), pharmacy and medication factors, and operational or policy drivers such as pharmacy closures, formulary and copay changes, and CMS rule or measure changes.
• Build and interpret explanatory models: Fit and interpret multivariable statistical models to identify which factors are significant when considered together, distill them into focused lists of drive, and explain why effects differ across populations, contracts, and performance years. Throughout, you will exercise judgment around confounding, selection bias, non-monotonic effects, and data quality. You will collaborate with cross-functional teams made up of data scientists, clinicians, and others.
• Quantify and prioritize opportunities: Identify, size, and rank intervention and member-targeting opportunities, informing which clinical tasks Arine prioritizes and surfaces, by weighing potential impact, feasibility, and likelihood of real-world adoption.
• Evaluate program and intervention impact: Measure the effect of Arine's programs, from adherence interventions to comprehensive medication reviews for high-risk members, on adherence, quality, utilization, and cost outcomes. You will use appropriate quasi-experimental and descriptive approaches (for example, year-over-year and cross-contract comparisons, difference-in-differences, and matching), accounting for population shift, risk-mix differences, and policy changes.
• Work in depth with real-world data: Explore, validate, and analyze claims, enrollment, and supplemental datasets. You will take ownership of data quality and proactively investigate anomalies before they reach a client.
• Translate findings into client deliverables: Turn analyses into clear, actionable recommendations and polished deliverables, such as executive status decks, written reports, and briefings, tailored to clinical and non-clinical audiences.
• Communicate with stakeholders: Present findings to internal cross-functional teams, including executives.
• Calibrate rigor to the question: Choose the right level of analytic rigor for the decision at hand, from descriptive and exploratory analysis through quasi-experimental designs, and participate in team reviews of methods, study design, and analytic approach.

Example Questions You Might Tackle:

• Which member, clinical, and social factors are the strongest leading indicators of non-adherence risk once you control for everything else, and do those drivers hold across different plans and populations?
• How much of a contract's year-over-year adherence change is explained by population shift, risk mix, formulary changes, or CMS rule changes versus program performance?
• Which medication conversion, outreach, or tasking opportunities offer the greatest realistic improvement in adherence and cost, and for which member segments?

Minimum Qualifications:

• Master's degree in Economics, Health Economics, Health Services Research, Epidemiology, Statistics, or a related quantitative field (PhD preferred).
• 3+ years of experience conducting research with real-world healthcare data (for example, medical and pharmacy claims) at a population level.
• Demonstrated experience researching medication adherence and outcomes across one or more of commercial, Medicare Advantage, or Medicaid Managed Care populations.
• Hands-on proficiency with SQL, R, and Python for manipulating data, conducting analyses, and creating graphics.
• Strong applied statistics: building and interpreting multivariable models (for example, logistic and linear regression), performing feature and variable selection, and clearly distinguishing correlation from causation.
• Working knowledge of causal inference for observational data, with the ability to apply methods such as difference-in-differences and matching to real-world client analyses, and to reason about confounding and selection bias.
• A self-directed, independent researcher who exercises good judgment, prioritizes multiple projects, and problem-solves under deadlines and ambiguity.
• Excellent written and verbal communication skills, including the ability to convey complex technical concepts clearly to audiences with varying levels of technical understanding.

Nice to Have:

• Familiarity with adherence and quality frameworks such as CMS Star Ratings and Part D adherence measures (diabetes, RAS antagonists, statins), SUPD and SPC, and HEDIS or Medicaid quality measures.
• Understanding of medication therapy classes and clinically appropriate alternatives (for example, antihypertensive and diabetes regimens) and how clinical context affects adherence measurement.
• Familiarity with clinical comorbidity indices, behavioral health indicators, and social determinants of health (for example, the Social Vulnerability Index) in real-world data.
• Experience producing client-facing deliverables and presenting to external or executive stakeholders.
• Prior experience in managed care, pharma, or a high-growth health-tech or start-up environment.
• Familiarity with healthcare data standards and ontologies (for example, ICD-10 and NDC).
• Familiarity with time-to-event and survival analysis (for example, time-to-first-gap and medication persistence) as a richer lens than a binary end-of-year adherence flag.

Bonus: Advanced Methods & Health-Economics Skills:

Not required, but any of the following would distinguish a candidate and shape where this role can grow:

• Advanced causal and quasi-experimental designs beyond standard difference-in-differences, such as target-trial emulation, regression discontinuity, synthetic control, or instrumental variables. This includes using natural experiments (such as pharmacy closures or formulary changes) and validation techniques like negative-control outcomes or falsification tests.
• Experience estimating heterogeneous treatment effects or conducting subgroup analyses to understand which members benefit most from an intervention, in close partnership with the Data Science team.
• Ability to connect adherence to downstream outcomes and economics, including modeling the path from adherence to utilization to total cost of care so that improvements carry a dollar value and an avoided-event count.
• Familiarity with healthcare cost and value methods, such as cost-effectiveness or budget-impact analysis, avoidable-spend attribution, or translating quality-measure gains (for example, CMS Star Ratings) into plan economics.
• Experience designing and analyzing experiments, such as A/B tests, stepped-wedge, or other randomized rollouts (for example, of outreach strategies), to generate experimental rather than purely observational evidence.

Scope of the Role:

This role is an applied health economics and outcomes researcher. To set clear expectations, the role focuses on the following and is distinct from adjacent roles on the team:

• Quantitative researcher, not a clinician: Clinical subject-matter expertise is provided by our PharmD and MD clinical researchers. You bring the quantitative and economic lens and collaborate closely with them.
• Explanatory research, not production prediction systems: You will build explanatory and inferential models (regression, quasi-experimental designs, and benchmarking) to understand drivers and quantify impact. Developing and deploying the production predictive prioritization and end-of-year forecasting models (for example, XGBoost), causal machine-learning methods, and simulation infrastructure that run inside the platform sits with the Data Science team. You will partner with, interpret, and inform that work rather than own it.
• Research, not data engineering: You will partner with engineering for data infrastructure and pipelines rather than owning them.
• Individual contributor: This is an individual-contributor role without direct reports.

Perks:

Joining Arine offers you a dynamic role and the opportunity to contribute to the company's growth and shape its future. You'll have unparalleled learning and growth prospects, collaborating closely with experienced Clinicians, Engineers, Software Architects, and Digital Health Entrepreneurs.

The posted range represents the expected salary for this position and does not include any other potential components of the compensation package, benefits, and perks. Ultimately, the final pay decision will consider factors such as your experience, job level, location, and other relevant job-related criteria. The salary range for this position is: $130,000-150,000/year.