Arine

Arine is a comprehensive medication management platform that empowers health plans and providers to improve patient outcomes at scale. By optimizing medication therapy and treatment plans, Arine helps reduce healthcare costs, increase member satisfacti...

Insurance

Financials

11-50 (30)

$47M raised

4 open positions

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Senior Data Scientist

6 hours, 7 minutes ago

United States

Full-time

Senior

Data Scientist

Data Science and Analytics

Machine Learning NumPy Pandas Python R Scikit-learn SQL

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Arine

Insurance

11-50

$47M raised

View All Jobs 4

Description

Design and execute causal analyses of real-world health data to quantify the impact of medications and interventions on patient outcomes and costs.
Develop, validate, and deploy predictive and causal models and AI tools into Arine's medication management platform.
Account for bias, confounding, and generalizability when building analytical solutions.
Partner with clinicians and HEOR researchers to define causal estimands and translate findings into clinical and operational decisions.
Create presentations and reports that communicate methods, assumptions, and results to technical and cross-functional stakeholders.
Participate in internal reviews covering methods, study design, implementation strategy, and code.
Collaborate with the Data Science team, in-house clinicians, engineers, and digital health stakeholders on analyses.

Requirements

Master's or PhD in Statistics, Biostatistics, Epidemiology, Health Economics, Computer Science, a quantitative/physical science, or a related field.
Strong foundation in statistical inference and study design, with demonstrated expertise in causal modeling with observational data.
Hands-on experience with causal machine learning methods such as double/debiased machine learning, causal forests, TMLE, or meta-learners.
Familiarity with causal inference libraries such as WeightIt, MatchIt, grf, DoWhy, EconML, or CausalML.
3+ years of experience working with real-world healthcare data such as claims or EHR data and analyzing patient outcomes.
Experience handling missingness, confounding, selection bias, and measurement error in real-world data.
Proficiency with R, Python, including scikit-learn, pandas, and numpy, and SQL.
Experience working cross-functionally to define analysis objectives and deliver analytical results.
Ability to work independently, prioritize multiple projects, and problem-solve under tight deadlines and resource constraints.
Excellent written, interpersonal, and presentation skills for communicating complex technical concepts clearly.
Ability to pass a background check.
Must live in and be eligible to work in the United States.
Bonus: experience modeling cost-of-care outcomes in high-risk, high-cost populations.
Bonus: deep experience with advanced difference-in-differences methods and quasi-experimental designs such as staggered/event-study DiD or synthetic control.
Bonus: productive use of AI-enhanced coding tools such as Claude Code.
Bonus: familiarity with survival analysis and longitudinal or repeated-measures methods.
Bonus: experience deploying models into commercial software products.

Benefits

Salary range of $160,000-$180,000 per year.
Opportunity to contribute to a rapidly growing healthcare technology company.
High-growth role with substantial learning and career development opportunities.
Close collaboration with experienced clinicians, engineers, software architects, and digital health entrepreneurs.
Dynamic environment with the chance to help shape the company from the ground up.

Interested in this position?

Apply directly on the company website

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