Data Scientist II en Medica

Medica is a nonprofit health plan with more than a million members that serves communities in Minnesota, Nebraska, Wisconsin, Missouri, and beyond. We deliver personalized health care experiences and partner closely with providers to ensure members are genuinely cared for.

We're a team that owns our work with accountability, makes data-driven decisions, embraces continuous learning, and celebrates collaboration — because success is a team sport. It's our mission to be there in the moments that matter most for our members and employees. Join us in creating a community of connected care, where coordinated, quality service is the norm and every member feels valued.

As a Data Scientist II at Medica, you will lead initiatives in predictive modeling, member segmentation, identification and stratification (ID/Strat), and program evaluation to support population health strategies and enterprise-level decision-making. You will also work with Git repositories and collaborate across teams to drive data governance and process improvements. 

Key Accountabilities

• Design and implement machine learning models for real-time forecasting of healthcare utilization and cost trends.
• Research and apply prescriptive solutions using data science and statistical methods to enhance modeling outcomes. 
• Develop and validate predictive models using ensemble methods and deep learning techniques. 
• Collaborate with internal stakeholders to develop data governance strategies and build robust data science pipelines.
• Collaborate with actuarial, finance, and enterprise analytics teams to embed predictive outputs into planning and decision-making processes.
• Support infrastructure design in collaboration with IT (e.g., Snowflake, Azure) to ensure scalable, secure, and governed model deployment.

Candidate Profile

The successful candidate will demonstrate leadership in complex healthcare analytics projects, possess strong technical expertise in statistical modeling and data engineering, and exhibit strategic thinking and innovation.

Required Qualifications

• Bachelor's degree or equivalent experience in related field
• 3+ years of work experience beyond degree in advanced analytics, statistics, or data science, preferably within healthcare.
  

Preferred Qualifications

• Masters in a quantitative discipline.
• Demonstrated experience in healthcare-related projects.
• 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.

This position is an Office role, which requires an employee to work onsite at our Minnetonka, MN office, on average, 3 days per week.

The full salary grade for this position is $70,700 - $121,200. While the full salary grade is provided, the typical hiring salary range for this role is expected to be between $70,700 - $95,950. Annual salary range placement will depend on a variety of factors including, but not limited to, education, work experience, applicable certifications and/or licensure, the position's scope and responsibility, internal pay equity and external market salary   In addition to compensation, Medica offers a generous total rewards package that includes competitive medical, dental, vision, PTO, Holidays, paid volunteer time off, 401K contributions, caregiver services and many other benefits to support our employees.

The compensation and benefits information is provided as of the date of this posting. Medica’s compensation and benefits are subject to change at any time, with or without notice, subject to applicable law.

We are an Equal Opportunity employer, where all qualified candidates receive consideration for employment indiscriminate of race, religion, ethnicity, national origin, citizenship, gender, gender identity, sexual orientation, age, veteran status, disability, genetic information, or any other protected characteristic.