Note: The job is a remote job and is open to candidates in USA. NORC at the University of Chicago is a non-partisan research institution focused on delivering reliable data and rigorous analysis. They are seeking a Senior Data Scientist II to lead analytic work using large administrative and claims-based data to support health care initiatives and reforms. This role involves technical leadership, mentoring, and collaboration with various stakeholders to enhance health care delivery systems.

Responsibilities

• Lead advanced analytic work supporting the implementation, evaluation, and monitoring of CMS Innovation Center (CMMI) models, including alternative payment models and delivery system reforms
• Lead projects and manage major project workstreams including but not limited to advanced analytic work supporting the implementation, evaluation, and monitoring of CMS Innovation Center (CMMI) models, including alternative payment models and delivery system reforms
• Apply claims-based and administrative data to assess program operations, utilization, quality, access, and cost across Medicare, Medicaid, and commercial populations
• Lead analyses using Medicaid T-MSIS (including TAF), state Medicaid claims and encounter data, Medicare fee-for-service claims, Medicare Advantage encounter data, and Medicare Part D prescription drug data
• Access and analyze data in CMS secure data environments, including CCW/VRDC and, as appropriate, Model Space, IDR, and other CMS data platforms
• Lead data acquisition, ingestion, cleaning, transformation, linkage, and harmonization for large and complex health care datasets
• Develop and implement quality assurance protocols, documentation standards, and reproducible analytic workflows
• Serve as a technical lead and mentor to data analysts and data scientists
• Lead or co-lead analytic and data-focused components of business development efforts, including proposal writing and capture discussions

Skills

• Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Economics, Public Health, or a related field
• At least 9 years of relevant experience (or 12 years with a Bachelor's degree) in positions of increasing responsibility involving large-scale data analysis and statistical modeling
• Demonstrated experience leading project teams or teams conducting substantial work streams on larger projects
• Demonstrated experience working with Medicare and/or Medicaid administrative or claims data
• Experience accessing and analyzing CMS data through the Chronic Conditions Warehouse (CCW) / Virtual Research Data Center (VRDC)
• Demonstrated ability to lead complex analytic work, mentor technical staff, and communicate results to technical and non-technical audiences
• Qualified applicants must be eligible to work in the U.S. We regret that we are unable to offer visa sponsorship for this position
• Master's degree or higher in a relevant field
• Experience supporting CMS Innovation Center (CMMI) model implementation, monitoring, or evaluation
• Experience working with CMS data environments, including Model Space and the Integrated Data Repository (IDR)
• Hands-on experience with T-MSIS/TAF, state Medicaid claims and encounter data, Medicare Advantage encounter data, and Medicare Part D prescription drug data
• Proficiency with SAS, SQL, R, and/or Python, and experience working in modern data warehouse or data lake environments
• Experience leading or contributing to technical proposal writing for federal health care analytics or evaluation work

Benefits

• Generously subsidized health insurance, effective on the first day of employment
• Dental and vision insurance
• A defined contribution retirement program, along with a separate voluntary 403(b) retirement program
• Group life insurance, long-term and short-term disability insurance
• Benefits that promote work/life balance, including generous paid time off, holidays; paid parental leave, bereavement leave, tuition assistance, and an Employee Assistance Program (EAP).
• This role is bonus eligible. Bonus payment is contingent upon program terms and individual performance.

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