Manager, Risk Adjustment Data Science

Mosaic Health, LLC.; New York (NY); On-site; USD 138,000 - 207,000; Full time

Job summary

Mosaic Health, LLC. in New York, NY is seeking a Manager, Risk Adjustment Data Science to drive advanced analytics and ML-driven solutions for clinical and business challenges.

You will lead end-to-end data science projects, collaborate with data engineering and business partners, and ensure enterprise readiness of models and insights. The role emphasizes problem framing, model deployment, and strategic recommendations for senior leadership, with opportunities to mentor a growing data science

Qualifications

• Own the development and productionization of advanced machine learning models and analytical frameworks addressing complex business and clinical problems.
• Lead end-to-end data science projects from problem framing through model deployment with cross-functional coordination.
• Design experiments, evaluation frameworks, and model validation approaches to ensure performance and business impact.
• Serve as the primary data science resource translating findings into strategic recommendations for senior leadership.
• Identify opportunities to apply data science methods to unaddressed problems and develop business cases for new initiatives.
• Establish and maintain best practices for model documentation, version control, and performance monitoring.

Responsibilities

• Own development and productionization of ML models and analytical frameworks.
• Lead end-to-end data science projects from problem framing to deployment.
• Design experiments and validation approaches to meet performance thresholds.
• Mentor and guide data scientists and analysts to elevate the team's capabilities.

Skills

Advanced analytics

Machine learning

Statistical modeling

Experiment design

Data wrangling

Job description

## Manager, Risk Adjustment Data ScienceApply: New York, USA: Full time: Posted Today: M105210**Job Description Summary**Responsible for applying advanced analytics, machine learning, and statistical modeling to solve complex business problems. Designs experiments, builds predictive models, and works with large datasets to uncover patterns and drive innovation. Collaborates with business and technical teams to integrate data science solutions into organizational workflows.

Lead Data Scientist is a recognized subject-matter expert who operates with minimal supervision and is the primary point of accountability for an area of data science work. Decisions impact function-wide results. This job proactively addresses undefined problems, makes recommendations that influence functional strategy, and synthesizes input across multiple stakeholder groups.**How will you make an impact & Requirements****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:**$137,805.00to$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.*