Manager, Risk Adjustment Data Science

Millennium Physician Group

United States

Remote

USD 138,000 - 207,000

Full time

14 days+

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Job summary

Millennium Physician Group is seeking a Lead Data Scientist to apply advanced analytics, machine learning, and statistical modeling to solve complex business and clinical problems. You will design experiments, build predictive models, and work with large datasets to uncover patterns that drive innovation and enterprise decisions.

You will lead end-to-end data science projects, coordinate with data engineering, analytics, and business partners, and mentor junior scientists to raise the team’s

Responsibilities

  • Own the development and productionization of advanced machine learning models and analytical frameworks that address 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 to meet defined performance and business impact thresholds.
  • Serve as the primary data science resource for cross-functional partners, translating 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.

Job description

"
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:

$137,805.00


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.

"
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