Sr Analyst, Integrated Care Partnerships (DaVita IKC)

Posting Date

04/23/2026

2800 N Loop WestSte 800, Houston, Texas, 77092-8838, United States of America

Our team plays a critical role in ensuring the right patients are identified and supported at the right time by our care teams. We develop, evaluate, and enhance the analytics that power patient risk identification, event detection, and care prioritization across the Integrated Kidney Care (IKC) organization.
Our work directly informs clinical workflows, resource allocation, and patient outcomes.

As a Senior Analyst, specializing in population health informatics on this team, you will harness technology and data systems to manage, analyze and apply healthcare data to provide valuable insights about prioritization strategy, patient outcomes, and care delivery. These insights will help enhance the performance, accuracy, and efficiency of risk stratification processes. You will bring forward new ideas, evaluate existing data assets, optimize analytic workflows, and help stakeholders understand results and implications for patient care.

This role collaborates closely with analytics partners, operational leaders, clinicians, and product/technology teams in a highly cross-functional, fast-moving environment.

Essential Duties & Responsibilities

Risk Stratification & Analytics

• Develop, maintain, and optimize patient risk stratification logic and identification models.
• Evaluate existing risk models and processes to identify areas for improvement, increased accuracy, or operational efficiency.
• Explore new data sources and technologies to enhance patient identification and event detection capabilities.
• Analyze clinical, demographic, and utilization data to understand patterns that drive risk and future events.
• Monitor and report on the health and performance of risk stratification systems, including precision, recall, stability, and operational outcomes.

Data Management & Technical Execution

• Write SQL queries against clinical, claims, and operational data warehouses to support risk modeling, reporting, and ad-hoc analytics.
• Build and maintain dashboards, scorecards, and summary reports to track key performance indicators related to risk identification.
• Collaborate with data engineering and product teams to ensure data availability, quality, and efficient use of analytic resources.

Insights, Interpretation & Communication

• Translate analytical findings into clear, actionable insights for clinical, operational, and leadership stakeholders.
• Create polished communication materials (PowerPoint, dashboards, memos) that summarize findings and recommended actions.
• Partner with stakeholders to refine requirements, define success metrics, and ensure consistent interpretation of risk metrics and patient identification workflows.

Innovation & Process Improvement

• Proactively bring forward ideas to optimize data pipelines, model inputs, model logic, and prioritization frameworks.
• Identify opportunities to automate or streamline risk identification workflows.
• Stay informed about new analytic methods, tools, and health data sources relevant to risk stratification and patient targeting.

Key Competencies & Attributes

• Strong analytical curiosity and a desire to improve processes and outcomes.
• Ability to communicate complex concepts clearly to both technical and non-technical stakeholders.
• Excellent organizational skills and the ability to manage multiple priorities.
• Collaborative approach with strong relationship-building skills.
• Comfort working in a dynamic environment with evolving needs and priorities.
• Growth mindset with ambition to learn new skills and disciplines.

Qualifications

Senior Analyst Level:

• Bachelor’s degree in statistics, analytics, public health, operations research, or related field.

• 3–5+ years of relevant analytics experience, or Master’s degree plus 2+ years of experience.
• Demonstrated experience leading analytic projects and presenting to leadership.

Technical Skills (Both Levels):

• Proficiency with SQL and relational databases.
• Strong understanding of statistical topics (probability, regression models, result interpretation, biases, etc.) and ability to interpret results into meaningful insights.
• Experience with Python, R, or other scripting languages strongly preferred.
• Experience working with clinical or medical claims data strongly preferred.
• Strong Excel skills for data manipulation/presentation.
• Experience with visualization tools such as Tableau or Power BI