About the role
We need an embedded data scientist contractor to establish the foundational data architecture for a quality intelligence network and convert fragmented audit, regulatory, policy, and reporting work into a connected operating model.
This role will begin in the Prior Authorization and Appeals experience and primarily support Audit Excellence & Strategy and Regulatory Management. The focus is to replace manual tracking, administrative documentation, and status reporting with governed data structures that enable analysis, root-cause identification, and structured process improvement.
The contractor must work across inconsistent, manually maintained, and highly regulated inputs. They will build scalable, usable structures for requirement management, metric standardization, traceability, issue visibility, and enterprise-quality reporting. The outcome is a shift from administrative coordination to proactive quality management and reusable system design.
- What this contractor must achieve
- Stand up the foundational data model: Create and refine the core tables required to launch the program, including requirements, controls, metrics, artifacts, issues, and results.
- Build a reliable requirements source of truth: Establish a single, reusable source of truth for requirements across setup, reporting, policy, job aids, and related artifacts.
- Reduce manual audit and regulatory administration: Design structures and logic that eliminate repetitive reconciliation, reduce spreadsheet dependency, and remove one-off work.
- Enable teams to focus on meaningful quality work: Free Audit and Regulatory teams from administrative tracking so they can focus on issue analysis, coordination, and process improvement.
Support Regulatory Management's team in high-friction work: Design structures that help Regulatory Management:
answer policy and mandate-based questions generate consistent requirement sets for PA implementation teams improve traceability across requirements, setup, policies, and reporting support state reporting and licensure data needs reduce the operational burden of policy management
Skills and profile required
Must Have
- Strong SQL and Python
- Data modeling and table design for complex operational environments
- Experience working with inconsistent or manually maintained data
- Ability to convert language-based requirements into structured logic
- Experience supporting reporting, compliance, audit, or regulated operations
- Ability to work independently in high-ambiguity environments
- Strongly preferred
- Certified Data Management Professional (CDMP)
- Microsoft Certified: Azure Data Scientist / Data Engineer
- AWS Certified Data Engineer or Data Analytics
- Healthcare, PBM, PA, Appeals, or pharmacy operations experience
- Experience with regulatory (CMS, NCQA, URAC, ERISA, state DOI), policy, or audit data
- Experience building self-service tools and reusable data assets
- Ability to design for governance, traceability, and audit defensibility
- What “done” looks like
- Done means the first usable version of the program is operating and reducing friction across teams.
- Minimum viable success (what we need at least)
- Core tables are built and governed for requirements, controls, metrics, artifacts, and results
- Audit team no longer recreates manual documentation structures for each audit
- Regulatory team can retrieve and package requirement sets from a structured source
- Reporting and requirement ownership gaps are clearly visible
- At least one high-friction workflow is improved end to end
Strong success (what we want)
- A reusable requirements repository exists with traceable linkage to policy, setup, and reporting
- Manual reconciliation and recurring spreadsheet work are reduced for a major workflow
- Leadership can view a clear inventory of reporting and requirement outputs with defined ownership
- Teams begin requesting expansion of the model
Transformational success (what will amaze us)
- Audit team spends significantly less time on administrative tracking and more time on analysis and improvement
- Regulatory team delivers consistent, reliable requirement sets for implementation
- A functioning quality intelligence network exists, not just isolated tables or dashboards