Engagement Manager - Healthcare Integrity​/AI Analytics

Role Overview

Fractal Analytics is a strategic AI partner to Fortune 500 companies. We help enterprises use data, analytics, and AI to improve decisions, operations, and business outcomes.

Role Overview

We are looking for a hands-on Engagement Manager to lead a strategic healthcare payer analytics engagement focused on Payment Integrity, claims analytics, and AI/ML-led claim review prioritization.

This is not a pure project management or account management role. The person must manage client stakeholders and program governance while also contributing hands-on to analytics solution design, model review, validation, and delivery quality.

The right candidate should have the technical depth to review analytical approaches, challenge model outputs, understand Databricks-based delivery, guide feature engineering discussions, and explain model results to business stakeholders.

Client & Stakeholder Management

• Serve as day-to-day engagement lead and primary point of contact for client stakeholders.
• Build trusted relationships with Payment Integrity, claims operations, analytics, clinical/coding, audit, SIU, and technology stakeholders.
• Lead working sessions, weekly governance meetings, executive reviews, and decision forums.
• Translate client priorities into clear analytical, technical, and delivery actions for Fractal teams.

Hands-on Analytics & Technical Delivery

• Work hands-on with data science and data engineering teams on healthcare claims analytics and AI-led scoring solutions.
• Review and guide analytical approaches for procedure cohorting, provider benchmarking, anomaly detection, risk scoring, and claim prioritization.
• Support feature engineering using claim header, claim-line, provider, procedure, billing, and review feedback data.
• Review model outputs for business usefulness, explainability, false positive patterns, and operational reviewability.
• Partner with technical leads on Databricks notebooks, pipelines, data profiling, scoring workflows, validation artifacts, and handover documentation.

Program Delivery & Governance

• Own engagement governance, delivery planning, milestone tracking, status reporting, scope management, and risk/issue management.
• Coordinate onshore and offshore teams to ensure clear ownership and disciplined execution.
• Prepare weekly status reports, monthly executive summaries, milestone readouts, and scale recommendations.
• Track team allocation, utilization, budget/resource status, and delivery burn.

Validation, Adoption & Scale

• Lead validation cycles with client reviewers and SMEs.
• Capture reviewer feedback, false positive themes, reason-code gaps, itemized bill needs, and operational improvement areas.
• Help refine scoring outputs, reason codes, anomaly flags, claim review rosters, and review recommendations.
• Connect model outputs to operational impact, workflow adoption, ROI framing, and scale readiness.

Must Have Skills

• 10+ years of experience in analytics delivery, data science delivery, technical program leadership, engagement management, consulting delivery, or similar client-facing roles.
• At least 5 years of experience in healthcare payer, claims, Payment Integrity, provider analytics, audit, SIU, clinical/coding review, fraud/waste/abuse, or medical cost management.
• Hands-on experience with analytics, data science, or ML delivery; this role requires technical proficiency, not just program coordination.
• Strong understanding of healthcare claims data, including claim header, claim-line, provider, procedure, diagnosis, billed/allowed/paid amounts, and review outcome data.
• Proficiency with Databricks or similar cloud data platforms.
• Strong working knowledge of SQL and Python;
  PySpark experience is strongly preferred.
• Experience with feature engineering, exploratory data analysis, data profiling, model scoring, validation, and analytical QA.
• Experience with ML methods such as anomaly detection, outlier scoring, provider benchmarking, risk scoring, classification, clustering, or ensemble-based modeling.
• Ability to review model outputs, identify data/model quality issues, and guide refinement with data science teams.
• Ability to explain analytical and ML outputs to non-technical business stakeholders.
• Strong program governance skills, including project planning, RAID management, milestone tracking, executive reporting, and escalation management.
• Ability to be onsite at client locations in NYC / Connecticut 3-4 days per week.

Good to Have Skills

• Experience with Payment Integrity analytics, itemized bill review, claims editing, provider outlier detection, procedure-level claims analytics, or claim review prioritization.
• Experience building or guiding ML-led prioritization, anomaly detection, provider benchmarking, reason-code generation, or explainability frameworks.
• Experience with MLflow or similar model tracking tools.
• Experience with production-readiness planning, pipeline QA, runbooks, model handover, and operational support.
• Experience with Snowflake, Azure, AWS, GCP, or similar enterprise data/AI…