HEOR Researcher

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.
• 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.

Quantify and Prioritize Opportunities

• Identify, size, and rank intervention and member-targeting opportunities, informing which clinical tasks Arine prioritizes and surfaces.
• Weigh 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.
• 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.

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.

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.