Lead Data Scientist

Lead Data Scientist - Causal Inference

Salary: $180,000 - $200,000

Location: New York Metro Area - Fully Remote (east coast preference)

About the Company

Our client is transforming healthcare decision-making through advanced statistical modeling and data-driven insights. They collaborate with healthcare providers, insurers, and policy organizations to optimize healthcare quality and financial outcomes. Backed by major investors and strategic partnerships, they leverage cutting-edge analytics to drive efficiency and effectiveness in healthcare delivery. Their work is deeply focused on claims data, economic modeling, and causal inference rather than biological or pharmaceutical research.

The Role

We are seeking a Lead Data Scientist with expertise in causal inference and claims data analysis. The ideal candidate has strong programming skills in R, SQL, Python, and Spark and a background in health economics, statistics, or econometrics. This role involves designing and refining scalable data pipelines, conducting rigorous statistical analysis, and translating data insights into actionable strategies.

Responsibilities

• Analyze large-scale claims data to evaluate the financial and quality-of-care impact of healthcare programs.
• Develop and maintain scalable data pipelines that support statistical modeling and business reporting.
• Apply causal inference techniques (e.g., difference-in-differences, propensity score matching) to assess healthcare interventions.
• Enhance ETL processes and optimize computational efficiency for large datasets.
• Collaborate with cross-functional teams to translate data-driven findings into policy and operational recommendations.
• Present findings to internal and external stakeholders in a clear, actionable manner.

Your Background

• **PhD or M.S. **in Statistics, Biostatistics, Economics, Data Science, Applied Mathematics, or a related field.
• Proficiency in R, Spark, SQL, and Python, with a strong emphasis on R for statistical modeling.
• Hands-on experience working with medical claims data, including ICD codes, risk adjustment, and healthcare cost analysis.
• Deep understanding of Generalized Linear Models (GLMs), mixed models, and longitudinal data analysis.
• Experience optimizing scalable data pipelines and ETL processes.
• Exposure to healthcare policy analytics, payer-side economics, or provider financial modeling.
• Experience in consulting, payer organizations, or healthcare analytics is a plus.
• Familiarity with machine learning applications in healthcare (classification, clustering, anomaly detection) is a plus.

How to Apply

If you believe you are a good fit given the above qualifications, send your resume to Cassie via the link below.

Keywords

Data Science, Healthcare Analytics, Claims Data, Statistical Modeling, Causal Inference, R, SQL, Python, Spark, Generalized Linear Models, Machine Learning, Health Economics, ETL, Big Data, Policy Analytics, Payer Economics, Healthcare Cost Analysis