Sr Analyst, Statistical Inference and Data Science (IKC)

Posting Date

03/27/2026

2000 16th Street, Denver, Colorado, 80202, United States of America

Denver, Colorado  |  Full-Time  |  On-Site/Hybrid 
 
About the Team 
The Commercial Integrated Care Analytics team supports DaVita Integrated Kidney Care (DaVita IKC). DaVita IKC is the renal population health management division of DaVita serving approximately 70,000 end stage renal disease (ESRD) and late stage chronic kidney disease (CKD) patients across the U.S. A key component of DaVita’s integrated care strategy is our healthcare analytics team, responsible for developing and communicating advanced data analytics to inform program performance and model of care design. 

As a member of this team, you will be responsible for designing and executing rigorous statistical analyses and data science work that measures the causal impact of our care model on clinical and cost outcomes. This entails developing inferential frameworks, building predictive models, and communicating evidence-based findings that drive care program decisions and value-based contract performance. 

This role will work with teammates across various teams, ranging from analyst to clinicians and operators to executive leaders. The environment is highly collaborative and team-oriented, with a differential focus on both professional and personal growth. 
 
Essential Duties and Responsibilities 
The analyst must be able to work successfully with cross-functional teams and have the maturity to interact directly with both peers and leaders across departments. The following duties and responsibilities generally reflect the expectations of this position, but are not intended to be all-inclusive. 

Statistical Inference & Study Design 

• Design and execute observational studies using quasi-experimental methods (e.g., difference-in-differences, propensity score matching, regression) to evaluate the impact of care interventions on clinical and cost outcomes 

• Build and interpret regression models (e.g., negative binomial, logistic, mixed-effects) to estimate treatment effects and identify drivers of utilization and cost 

• Apply Bayesian or frequentist inference frameworks to quantify uncertainty in program effectiveness estimates 

• Conduct power analyses and sample size calculations to support pilot study design and program evaluation planning 

• Develop analytic designs that translate operational research questions into well-specified studies, including defining appropriate comparison groups and controlling for confounders 

Data Science & Model Measurement 

• Build, validate, and monitor predictive models (e.g., hospital readmission risk, disease progression, cost forecasting) that inform care management priorities and resource allocation 

• Develop and maintain model performance measurement frameworks, including calibration, discrimination, and fairness metrics, to ensure models remain accurate and actionable over time 

• Conduct feature engineering using clinical, claims, and demographic data to improve model performance and interpretability 

• Evaluate and compare modeling approaches (e.g., penalized regression, gradient boosting, survival analysis) with attention to explainability and clinical relevance 

• Partner with operations and clinical teams to translate model outputs into practical decision support tools and intervention triggers 

AI Integration & Workflow Innovation 

• Incorporate large language models and AI-assisted tooling into analytic workflows to accelerate code development, literature review, documentation, and exploratory analysis 

• Evaluate emerging AI tools and methods for applicability to healthcare analytics use cases, including automated feature selection, synthetic data generation, and natural language processing of clinical notes 

• Develop and share best practices for responsible AI use within the analytics team, including prompt engineering, output validation, and appropriate use-case scoping 
  Communication & Stakeholder Engagement 

• Produce clear, technically sound documentation of analytic methods, assumptions, and limitations 

• Develop executive-level communications that translate complex statistical findings into actionable insights for clinical, financial