Senior Data Scientist, Causal Inference

About the company

At Covera, we're committed to ensuring high-quality healthcare is more than just a promise. That's why we're leading the way in the emerging science of quality, and connecting providers and payers in their shared quest to improve patient outcomes and care quality. By tackling this challenge, we have the ability to impact millions of lives by raising the standard of care nationwide.

Our initial focus is radiology, where an early and accurate diagnosis has a profound impact on the rest of a patient's care journey. Through our work, which uses clinically-validated science-based tools, we're helping doctors enhance their care, ensuring patients get the right diagnosis, and enabling the healthcare system to support quality improvement at scale.

Through our clinical intelligence platform, we have launched programs that help people access the most effective care and provide doctors with AI-powered quality insights and tools to enhance their care. Today, Covera is partnered with leading employers, payers and healthcare organizations across the US, including Walmart and Microsoft. And, with a pipeline representing over 25% of insured Americans, we are in the early stages of improving care quality for all patients across the globe.

In November 2023, Covera secured up to $50 million in a Series C extension led by Insight Partners. This capital fuels our mission to partner with healthcare providers, payers and employers to improve diagnostic care for patients everywhere.

About the role

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.

Your Profile:

• Educational Background: Ph.D. or M.S. in Statistics, Economics, Biostatistics, Applied Mathematics, Epidemiology, Computer Science, or a related field.
• Experience: 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.
• Statistical and Causal Inference Expertise:
• 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.
• Coding Skills:
• 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.
• Experience developing scalable code for use by a wider team and cont