Senior Data Scientist - Causal Inference
Are you passionate about causal inference and it's application to healthcare Data? Do you want to work on cutting-edge statistical modeling and make a real impact in the healthcare space? If so, we’re looking for a Senior Data Scientist - Causal Inference to join our high-visibility, high-impact team tackling some of the most important challenges in diagnostic care and healthcare program evaluation.
Why Join Us?
Work with a leader in causal inference. Our team leader was a mentee of the 'Father of Causal Inference' Donald Rubin (Harvard).
High impact, high visibility. You’ll regularly engage with clients, sales teams, and objectives directly relayed from C-suite.
Cutting-edge healthcare analytics. We are defining new methodologies to quantify program ROI, leveraging advanced causal inference techniques.
Opportunity to grow. This role is designed to evolve into a Principal Data Scientist position in 1-2 years.
Collaborative and supportive culture. No egos—just brilliant minds working together to solve complex problems.
Well capitalized. We are a Series C Startup, backed by prominent VC.
Work where you work best. Work remotely from anywhere in the US, or out of our head office in NYC - your call.
What You’ll Do:
Develop and apply causal inference methodologies to quantify the impact of healthcare programs.
Co-lead technical efforts alongside our team leader, contributing to both theoretical and practical advancements.
Design and implement statistical models using techniques like difference-in-differences, propensity score matching/weighting, and regression discontinuity design.
Work with claims data (ICD codes, cost categorization, etc.) to analyze healthcare outcomes.
Code in R, SQL, and Python (our stack is predominantly R, with Spark and Sparklyr for distributed computing).
Engage with clients and stakeholders, balancing technical rigor with real-world business needs.
What We’re Looking For:
PhD in Statistics, Econometrics, Biostatistics, or a related field (or a Master’s with extensive experience).
Deep expertise in causal inference (difference-in-differences, propensity scoring, etc.).
Strong coding skills (R is a must; + SQL and Python).
Experience with healthcare claims data (ICD codes, cost structuring, payer-side analytics).
Familiarity with distributed computing frameworks (Spark, Sparklyr).
Client-facing experience and high emotional intelligence—you’ll work closely with external stakeholders.
Flexibility & pragmatism. If coming from academia, the ability to adapt to the fast-paced nature of a startup is key.
A team-first mentality—no egos, just a passion for solving tough problems together.
What Makes This Role Unique?
You’ll define how AI-driven healthcare program evaluation is done.
You’ll be a key player in our expanding team, influencing both strategy and execution.
You’ll work on highly theoretical, cutting-edge statistical problems while engaging with real-world applications.
You’ll directly shape the future of healthcare analytics, with your work being seen at the highest levels of the company.
Compensation:
If you’re excited about applying causal inference to real-world healthcare challenges and want to be part of a dynamic, high-impact team, we’d love to hear from you!
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