Medica is looking to solve problems in predictive modeling, member segmentation, identification and stratification (ID/Strat), and program evaluation to support population health strategies and enterprise-level decision-making.
Requirements
- Advanced modeling techniques (e.g., ensemble methods, deep learning).
- Expertise in SQL, Python, R, SAS.
- Familiarity with code source control (Git, GitLab, GitHub).
- Experience with cloud platforms and data science IDEs (e.g., Jupyter Lab, Anaconda, Spyder).
- Strong data engineering and pipeline development skills.
- 5+ years experience in advanced analytics, statistics or data science.
- Preferably within healthcare.
Responsibilities
- Develop and validate predictive models using ensemble methods and deep learning techniques.
- Create and manage member segmentation models using clinical, behavioral, and demographic data to inform care strategies.
- Design and implement program evaluations (value studies) to assess effectiveness and identify opportunities for improvement.
- Collaborate with internal stakeholders to develop data governance strategies and build robust data pipelines.
- Research and apply prescriptive solutions using data science and statistical methods to enhance program outcomes.
- Lead the production and governance of identification and stratification algorithms for population health.
- Score population health predictions and support program improvement initiatives.
Other
- Demonstrate leadership in complex healthcare analytics projects
- Exhibit strategic thinking and innovation.
- Comfortable influencing senior leadership and mentoring junior team members
- Maintaining a high standard of documentation and presentation.
- This position is an Office role, which requires an employee to work onsite at our Minnetonka, MN office, on average, 3 days per week.
