Lead Data Scientist – Strategic Initiatives

Role Overview

• Designs and develops of scalable solutions using AI tools and machine-learning models
• Performs research and testing to develop machine learning algorithms and predictive models
• Utilizes big data computation and storage tools to create prototypes and datasets
• Conducts model training and evaluation
• Integrates, tests, tunes, and monitors solutions
• Critically evaluate valuation processes to identify gaps or invalid assumptions
• Independently design and implement mathematically rigorous methodologies for measuring program efficacy
• Extract and manipulate large-scale healthcare data (claims, clinical, SDoH) to build independent, scalable valuation and predictive models
• Act as a strategic communicator, translating complex methodologies and financial impacts into compelling narratives for non-technical executive stakeholders
• Develop operational frameworks to translate clinical interventions into defensible financial forecasts and enterprise-wide statements
• Navigate extreme ambiguity to scope undefined business problems and transform raw data into production-ready valuation frameworks without an existing playbook
• Analyzes use cases of ML algorithms and apply findings to enable business decisions by deriving insights through data visualization
• Writing complex SQL and Python/R code to extract data from a variety of databases and data sources
• Conduct exploratory data analysis from complex data sources and build key data sets to support company mission operational analysis
• Builds complex ML/AI models using common methods within R and Python
• Analyzes the ML algorithms that could be used to solve a given problem and rank them by their success probability
• Develop logging, alerting, and mitigation strategies for handling model errors
• Evaluate and design experiments to monitor key model metrics and identify improvement opportunities
• Produces fairness review for each developed model, to ensure model is free of bias
• Set up continuous integration/continuous deployment (CI/CD) pipelines used for model automation
• Model deployment with containers such as Kubernetes and/or Docker
• Produce clean code that has been properly unit tested and optimized for performance
• Completes proper deployment integration testing to ensure seamless deployment
• Performs other duties as assigned

Requirements

• A Bachelor's degree in a quantitative field (e.g., statistics, mathematics, economics, engineering, computer science) required or equivalent experience
• Master's or PhD preferred
• Requires 5–7 years of progressively complex data science experience, with a proven track record of delivering high-impact solutions in healthcare or regulated environments
• 5+ years Intermediate knowledge & experience with SQL required
• 5+ years experience in developing Machine learning/AI/Predictive Models in R or Python required
• 5+ years Intermediate knowledge of Git required
• Experience using DataBricks platform preferred
• Experience in VBC data science, including shared savings models, risk adjustment, or clinical efficacy evaluation preferred
• Background in high-stakes environments translating complex data architecture and statistical results into executive-level strategy preferred

Tech Stack

• Docker
• Kubernetes
• Python
• SQL

Benefits

• health insurance
• 401K and stock purchase plans
• tuition reimbursement
• paid time off plus holidays
• flexible approach to work with remote, hybrid, field or office work schedules