Manager, Risk Adjustment Data Science

Key Responsibilities

• Own the development and productionization of advanced machine learning models and analytical frameworks that address the organization's most complex business and clinical problems. • Lead end-to-end data science projects from problem framing through model deployment, coordinating with data engineering, analytics, and business stakeholders to ensure enterprise readiness. • Design experiments, evaluation frameworks, and model validation approaches that ensure data science solutions meet defined performance and business impact thresholds. • Serve as the primary data science resource for cross-functional business partners, translating complex analytical findings into strategic recommendations for senior leadership. • Identify opportunities to apply data science methods to unaddressed business problems and develop business cases for new modeling initiatives. • Establish and maintain best practices for model documentation, version control, and performance monitoring across the data science team. • Provide mentorship and technical guidance to data scientists and analysts, elevating the team's modeling capability and analytical rigor.
• Compensation Range:
• $137,805.00
• to
• $206,708.00
• The anticipated base salary range represents the Company's good-faith estimate of the compensation it reasonably expects to pay for this position at the time of posting. Actual compensation will be determined based on factors including experience, skills, qualifications, geographic location, internal equity, and business needs.
• Own the development and productionization of advanced machine learning models and analytical frameworks that address the organization's most complex business and clinical problems.
• Lead end-to-end data science projects from problem framing through model deployment, coordinating with data engineering, analytics, and business stakeholders to ensure enterprise readiness.
• Design experiments, evaluation frameworks, and model validation approaches that ensure data science solutions meet defined performance and business impact thresholds.
• Serve as the primary data science resource for cross-functional business partners, translating complex analytical findings into strategic recommendations for senior leadership.
• Identify opportunities to apply data science methods to unaddressed business problems and develop business cases for new modeling initiatives.
• Establish and maintain best practices for model documentation, version control, and performance monitoring across the data science team.
• Provide mentorship and technical guidance to data scientists and analysts, elevating the team's modeling capability and analytical rigor.