Data Scientist III – Causal Inference

Data Scientist III – Causal Inference

Requirements

• M.S. or B.S. in Computer Science, Statistics, Biostatistics, Economics, Data Science, Applied Mathematics, or a related field.
• At least 2 years of experience for M.S. degree holders, or 5 years for B.S. degree holders.
• Strong foundation in data engineering and 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.
• Experience with fundamental data science models, including Generalized Linear Models (GLM), Mixed Models and longitudinal data analysis.
• Proven ability and enthusiasm for working in a collaborative team environment, paired with a proactive, go-getter attitude.
• Willingness to learn new techniques and tackle diverse data science challenges at the intersection of healthcare and analytics.

Responsibilities

• Process and Analyze Healthcare Data: Work with various types of healthcare data, including longitudinal claims data, to help teams quantify the relationships between healthcare quality and patient outcomes, such as cost, clinical outcomes, and care patterns.
• Maintain and Optimize Data Pipelines: Develop, run, and enhance data science and data engineering pipelines that create modeling datasets; run statistical models; and produce quarterly business reports.
• Improve and Troubleshoot Codebase: Review and optimize the team’s data ETL and statistical code, both legacy and in development, 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 Health’s strategy and decision-making.
• Advanced Statistical Modeling: Conduct advanced statistical modeling of claims data under the supervision of the team’s experts, utilizing methods like Generalized Linear Models, Matching for Causal Inference, and Difference-in-Differences. Apply statistical models and methods developed by the data science team to quantify program savings and ROI.
• Prepare and Communicate Insights: Assist in preparing documentation and presentation materials for client meetings and key deliverables, and effectively communicate analytical results to both internal and external stakeholders.
• Collaborate: Work closely with data science team members and other colleagues across Covera on data analysis requests, projects, and research initiatives.

Preferred Qualifications

• Experience with (or enthusiasm to learn) causal inference methodologies, such as propensity score matching and difference-in-differences, is a plus.
• Preferred experience working with payor organizations, healthcare consulting, and/or fast-paced, client-facing environments.