Supports AI and analytics discovery by analyzing operational data and process flows, identifying inefficiencies, assessing AI/ML use cases, and generating business insights. Responsibilities include data exploration, analytical modeling, process mining, dashboard and visualization development, data-readiness assessment, documentation of assumptions and limitations, and collaboration with data scientists, business analysts, consultants, and architects. Healthcare domain experience is required.
Role: Associate Data Scientist
Discovery Track: Agentic AI & Analytics Support
Role Summary: Supports AI and analytics discovery through process mining, AI use case analysis, data exploration, analytical modeling, and insight generation for CXI transformation opportunities.
Requirements
Key Responsibilities:
- Analyze operational data and process flows to identify bottlenecks, inefficiencies, patterns, and improvement opportunities.
- Support AI/ML and agentic AI use case analysis, prioritization, feasibility assessment, and value framing.
- Prepare analytical summaries, data-readiness observations, visualizations, dashboards, and insight packs.
- Collaborate with Principal Data Scientist, BAs, consultants, and architects to connect analytics findings to business opportunities.
- Document data assumptions, data quality observations, process mining outputs, and analytical limitations.
Required Qualifications:
- 3-6+ years in analytics, data science, process mining, or applied machine learning.
- Healthcare domain experience is required, with healthcare payer, claims, member experience, G&A, audit, or regulatory workflow exposure preferred based on track assignment.
- Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Engineering, or related field
- Experience with Python/R/SQL, analytics tools, visualization, process mining tools, and basic ML methods preferred
- Ability to translate data findings into clear business insights.
Key Competencies / Deliverables:
- Competencies: Process mining, AI use case analysis, data exploration, analytical modeling, dashboarding, insight generation, documentation.
- Primary deliverables: Process mining outputs, analytics pack, AI use case analysis, data-readiness notes, insight dashboards, assumptions/limitations log.
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