Senior Data Scientist (Causal Inference) @ HealthTech Startup

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:

• $175,000 - $200,000 Base Salary
• Annual Bonus
• Significant Equity
• 100% covered benefits (Dental, Vision, Health)

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!