You will:
- Work closely with the Head of Clinical Data Science to support advanced statistical analyses and interpretation
- Use a statistical software package (R, Python, etc) to run statistical analyses on healthcare utilization, engagement data, and claims cost analyses
- Build out the ontology and standardized data model for claims data
- Explore using statistical learning/machine learning/bayesian methods to predict cost savings, ER/inpatient utilization, and run simulation models to evaluate step therapies and disease progression from claims data
- Write codes to make quantitative associations (using regression analyses and causal inference methods)
- Developing and advancing methodologies to evaluate cost effectiveness and cost savings from claims data
- Assist in automation of codes/analytic processes
- Clean data and conduct quality checks
- Create and interpret data dictionaries, specifications, and other technical documentation share by or with internal and external stakeholders
You are:
- Outcome-driven problem solver: You are passionate about applying statistical and machine learning techniques to solve complex healthcare challenges, with a keen focus on delivering data-driven results.
- Analytical and decisive: You make data-informed decisions under pressure, balancing detailed analysis with sound judgment to pursue high-value initiatives while maintaining focus on project objectives.
- Adaptable and resourceful: You thrive in dynamic environments, finding creative, technically sound solutions under tight constraints without sacrificing data integrity or organizational values.
- Skilled collaborator: You excel at working cross-functionally, translating technical insights into actionable strategies for both technical and non-technical stakeholders to drive impactful outcomes.
You have:
- 2-6 years of experience in with Healthcare Claims (Medical, Rx) and Eligibility Files
- At least 2-4 years of experience with machine learning, causal inference; bayesian statistics and simulation modeling (in particular MCMC) is a plus
- Expert in R (tidyverse, caret, xgboost), Python (numpy, pandas, scikit-learn, statsmodel, SciPy), or similar statistical software package, and SQL (window functions, CTE’s, aggregate functions, etc.)
- Experience with claims analyses and with large, real-world datasets; not limited to merging datasets and cleaning raw data
- Undergraduate or Masters in biostatistics, epidemiology, statistics, data science, computer science, health economics, or equivalent degree
- Public health background is a plus!
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