Senior Data Scientist, Causal Inference

About the position

In this role, you will be expected to: Process and Analyze Healthcare Data: Work with various types of healthcare data, including longitudinal medical claims data, to quantify the relationships between healthcare quality and patient outcomes, such as cost, clinical outcomes, and care patterns. Conduct Statistical Analysis and Causal Inference Modeling: Conduct statistical modeling of claims data using methods like Propensity Score Matching, Propensity Score Weighting, and Difference-In-Differences. Develop, maintain, extend, and validate models and methods to quantify program savings and ROI. Provide Technical and Thought Leadership: Serve as an in-house expert on causal inference techniques, lead study design efforts, propose and develop Statistical Analysis Plans (SAPs), and ensure methodological rigor across projects. Maintain and Extend Data and Modeling Pipelines: Develop, run, and enhance our data engineering and modeling pipelines to create modeling datasets, run statistical models, and produce quarterly business performance reports. Improve and Troubleshoot Codebase: Review and optimize the team’s data pipelines and statistical code to enhance runtime and memory efficiency, ensure reproducibility, and support automation and scalability. Troubleshoot technical issues as they arise, working with the engineering team as needed for support. Conduct Ad-Hoc Analyses: Conduct ad-hoc analyses (e.g. of claims data) as business needs arise in a fast-paced environment to uncover business and clinical insights and support Covera’s strategy and decision-making. Prepare and Communicate Insights: Develop clear, compelling documentation and presentations for client meetings and key deliverables. Effectively communicate analytical results to both internal and external stakeholders and co-lead methodological discussions with clients. Collaborate: Work closely with data science team members and cross-functional colleagues across Covera on analysis requests, projects, and research initiatives. Publish: Author and contribute to academic publications and white papers. Serve as an ambassador for Covera’s methodologies and value proposition within the research and healthcare communities.

Responsibilities

• Process and Analyze Healthcare Data
• Conduct Statistical Analysis and Causal Inference Modeling
• Provide Technical and Thought Leadership
• Maintain and Extend Data and Modeling Pipelines
• Improve and Troubleshoot Codebase
• Conduct Ad-Hoc Analyses
• Prepare and Communicate Insights
• Collaborate
• Publish

Requirements

• Ph.D. or M.S. in Statistics, Economics, Biostatistics, Applied Mathematics, Epidemiology, Computer Science, or a related field
• At least 2 years of experience for PhD degree holders or 5 years for M.S. degree holders with a strong track record of applying statistical and causal inference methods to real-world healthcare data
• Strong foundation in statistical modeling, including Generalized Linear Models, Mixed Models, and longitudinal data analysis
• Expertise in study design and causal inference methodologies such as Propensity Score Matching, Propensity Score Weighting, Difference-In-Differences, and Regression Discontinuity Design
• Strong foundation in coding best practices, with expertise in R, Spark (specifically sparklyr), SQL, and Python for data science
• Exceptional skills in R and sparklyr are required
• Strong understanding of, and experience working with, real-world medical and claims data, including familiarity with ICD codes, CPT codes, CMS-HCC models, and comorbidity coding
• Excellent communication skills, with a proven ability to explain complex methodologies to non-technical stakeholders
• Enthusiasm for working in a collaborative, cross-functional team environment, paired with a proactive, problem-solving mindset

Nice-to-haves

• Preferred experience working with payor organizations, healthcare consulting, and/or fast-paced, client-facing environments

Benefits

• Competitive salary
• Stock options
• Medical, dental, and vision insurance
• HRA
• 401k
• Pre-tax commuter benefits
• Flexible paid time off
• Comfortable office space filled with various quality snacks and beverages