Senior Data Scientist
About The Position
This role involves deriving rigorous insights from large, diverse real-world health datasets to support Arine's mission to improve patient outcomes and lower the cost of healthcare at scale. You'll go beyond prediction to estimate the real effects of medications and interventions on patient outcomes using modern causal inference and causal machine learning methods. This is a dynamic role and an amazing opportunity to help shape the company from the ground up, with many growth opportunities. The Data Science team works with feature-rich, high-volume clinical datasets and collaborates cross-functionally with an experienced team of in-house clinicians, health economics and outcomes researchers (HEOR), engineers, and digital health entrepreneurs.
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, and demonstrated expertise in causal modeling with observational data, e.g., propensity score methods, inverse probability weighting, instrumental variables, difference-in-differences, regression discontinuity, marginal structural models, and target trial emulation
- Demonstrated, hands-on experience with causal machine learning methods, e.g., double/debiased machine learning, causal forests, targeted maximum likelihood estimation (TMLE), or meta-learners (T-/X-/R-learners)—and familiarity with libraries such as WeightIt, MatchIt, grf, DoWhy, EconML, or CausalML
- Meaningful experience (3+ years) working with real-world healthcare data (e.g., medical and pharmacy claims or EHR data) and analyzing patient outcomes
- Experience handling the practical challenges of real-world data: missingness, confounding, selection bias, and measurement error
- Proficiency with R, Python (scikit-learn, pandas, numpy), and SQL
- Experience working cross-functionally to define analysis objectives and deliver analytical results
- Demonstrated ability to work independently, exercise good judgment, prioritize multiple projects, and problem-solve under tight deadlines and resource constraints
- Excellent written, interpersonal, and presentation skills, including the ability to convey complex technical concepts clearly to audiences with varying levels of technical understanding
- Ability to pass a background check
- Must live in and be eligible to work in the United States
Nice To Haves
- Experience modeling cost-of-care outcomes (e.g., total cost of care, utilization, avoidable spend) in high-risk, high-cost populations
- Deep experience with advanced difference-in-differences methods and related quasi-experimental designs (e.g., staggered/event-study DiD, synthetic control)
- Productive use of AI-enhanced coding tools (e.g., Claude Code) in day-to-day development
- Familiarity with survival analysis and longitudinal/repeated-measures methods
- Experience deploying models into commercial software products
Responsibilities
- 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, with careful attention to bias, confounding, and generalizability
- Partner with clinicians and HEOR researchers to frame questions as well-defined causal estimands and translate findings into clinical and operational decisions
- Create presentations and reports communicating methods, assumptions, and results to the Data Science team and cross-functional stakeholders
- Participate in critical reviews within the Data Science team on everything from methods and study design to implementation strategy and code
Benefits
- Unparalleled learning and growth prospects
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What This Job Offers
Job Type
Full-time
Career Level
Senior
