Experience

From biomedical data to accountable AI workflows.

My scope has expanded from analysis and prediction to product leadership, local GenAI platforms, and agentic claims systems. The constant is making uncertainty operationally visible.

01

2026 — present

Insurance claims and agentic AI

MetLife via Bizintex

Applied AI Engineer Consultant

Designing governed document and claims workflows with typed contracts, validation gates, reviewer fallbacks, and trace telemetry.

Technical judgment

Separated model reasoning from deterministic workflow authority and kept uncertain cases in visible review states.

  • ≈90% lower document-handling effort in a measured claim-packet workflow
  • ≈50% shorter time-to-claim-payable in a supported workstream
02

2023 — 2025

Payer AI modernization

Inland Empire Health Plan via Infowave

Lead DS/ML Engineer Consultant

Led local RAG/OCR, healthcare quality evidence extraction, predictive ML, FWA analytics, MLOps, and operational reporting.

Technical judgment

Chose local inference when the data boundary required control, then built evaluation and reviewer paths around OCR and retrieval failure.

  • Cleared a 7K-case review backlog with ≈90% lower review time
  • Improved automated measure closures by 20%
  • Reduced transportation waste by 18%
03

2020 — 2023

0-to-1 healthcare analytics platform

Hexplora

Lead Data Scientist / Product

Built and scaled a predictive analytics platform across nine healthcare programs, combining risk models, data products, and care-manager workflows.

Technical judgment

Treated model delivery, workflow design, and reporting as one product problem instead of separate technical workstreams.

  • $500K in new revenue supported
  • ≈$3M in client performance-based payouts
  • ≈50% faster model deployment cycles
04

Earlier foundations

Research and operational foundations

CommonSpirit Health · Health New England · University of Chicago · AbbVie

Data science, biomedical informatics, and clinical-data work

Worked across hospital analytics, provider data, biomedical research, clinical sensors, genomics, and public-health modeling.

Technical judgment

Learned to make data lineage, statistical assumptions, and operational context visible before a model could be trusted.

  • Analytics spanning 142 hospitals
  • 100K+ provider records validated
  • 60% less manual provider-review effort

Impact ledger

Every public number keeps its context.

These values are drawn from the latest résumé and are not combined into a single-project story.

7K

case backlog cleared

On-premises compliance review workstream in a healthcare payer environment.

resume-supported
90%

review-time reduction

Measured document-review workflow after local retrieval and structured extraction were introduced.

resume-supported · Approximate reduction in the measured workflow; not an enterprise-wide claim.
20%

automated closure improvement

Healthcare quality-measure evidence extraction and review workflow.

resume-supported
18%

FWA waste reduction

Transportation anomaly detection and explainable review prioritization.

resume-supported
≈$3M

client P4P impact

Healthcare analytics programs supporting quality and value-based care delivery.

resume-supported · Approximate client performance-based payouts supported by the broader program.
$500K

new revenue enabled

0-to-1 healthcare analytics platform and client program delivery.

resume-supported

Education

Biomedical informatics and public-health foundations.

  • M.S., Biomedical Informatics — University of Chicago, 2019
  • Summer School, Public Health Modeling — Yale University, 2019
  • B.Tech., Computer Science & Engineering — SRM University, 2018

Selected certifications

  • AWS Certified Machine Learning Engineer — Associate
  • Microsoft Azure AI Fundamentals (AI-900)
  • Microsoft Power BI Data Analyst (PL-300)