Curriculum Vitae

AI engineering leader and applied scientist with 15+ years bridging frontier research and enterprise-scale production. PhD in Nanoscale Science and Engineering. Arlington, Virginia. phil@arrowofti.me · LinkedIn · ORCID 0000-0002-9997-0474

Education

PhD, Nanoscale Science and Engineering

State University of New York at Albany · 2005 – 2009

Graduate College Valedictorian.

BSc, Physics

California Polytechnic State University, San Luis Obispo · 2002 – 2005

Professional Experience

Alignment Healthcare — AVP, AI Engineering

Jan 2026 – Present

• Engineered a high-performance analytics data warehouse architecture achieving a 6× reduction in model runtimes, accelerating clinical insight cycles and reducing compute overhead.
• Scaled the AI engineering organization from 2 to 10 specialists, implementing recruiting rubrics and onboarding processes for rapid growth.
• Establishing the organization’s first formal AI Governance framework, reducing legal/compliance risk through rigorous model validation and audit trails for clinical AI.
• Translating LLM capabilities into product requirements for clinical stakeholders, improving patient care delivery and operational efficiency.

Peraton — Data Science Advisor (CDC Center for Forecasting and Outbreak Analytics)

Dec 2024 – Dec 2025

• Modernized the code stack and ModelOps framework, increasing model iteration speed by 3× and shortening time-to-deployment for public-health analytics.
• Optimized the issue-resolution pipeline, reducing critical issue closure times from one week to one day (85% improvement), ensuring high availability for CDC forecasting systems.
• Built cloud service abstractions scaling public-health analytics infrastructure, improving query latency and data accessibility for federal stakeholders.

Stealth AI Startup — Senior AI Consultant

Jun 2024 – Dec 2025

• Conceptualized and prototyped a Generative AI value-creation framework projected to reduce required engineering headcount from 10 to 3 while maintaining the same output velocity.
• Engineered a multi-agent system using advanced prompting and schema validation to automate complex business workflows via the n8n platform.
• Built real-time visualization dashboards for agent decision-tracking, cutting the time to debug agentic “hallucinations” and improving system reliability.

Clarify Health Solutions — Head of Data Science (Senior Director), then Independent Consultant

Nov 2020 – Dec 2025

• Patented and deployed a novel predictive analytics algorithm that reduced combined operational and cloud expenditures by ~$840K annually ($70K/month) while increasing model precision.
• Engineered a comprehensive model QA/QC reporting system that cut production errors by 75%, dramatically improving reliability of clinical insights.
• Scaled the data-science product portfolio from 2 to 5 core offerings, leading design and deployment of new agentic AI products.
• As consultant: provided strategic advisory for healthcare data-science initiatives; mentored teams and advised on organizational priorities and best practices.

EY — Machine Learning Architect, Platform Technical Lead (Assistant Director)

Sep 2019 – Nov 2020

• Architected an end-to-end AI platform for financial document processing (statements, prospectuses, contracts) that reduced manual review cycles from 10 hours to minutes (~99% processing-efficiency gain).
• Standardized AI project discovery, feasibility, and engineering processes with model documentation standards, reducing failed project starts and increasing production success rates.
• Established hiring guidelines for data scientists and the AI practice’s technical interview rubric.

Change Healthcare — AI Manager / Lead Data Scientist

Nov 2017 – Aug 2019

• Implemented an active learning framework that reduced labeling requirements by 80–90%, accelerating the model development lifecycle.
• Designed and deployed a custom DenseNet architecture for medical chart text classification, increasing accuracy from 80% (GBT baseline) to 99%+.
• Developed a shared AI component library, reducing foundational development time by 50% across all AI teams.

Accenture — R&D Technology Associate Principal, then Analytics & Modeling Associate Manager

Feb 2015 – Nov 2017

• Developed a rapid data labeling workbench that increased annotation velocity 3–10×, reducing the time to build ground-truth datasets.
• Leveraged Stanford University laboratory research to build high-impact prototypes instrumental in winning and retaining federal and commercial accounts.
• Enhanced the DHS National Biosurveillance Integration Center’s resource stack by integrating two new complex data streams for national health monitoring.

Cortana Corporation — Staff Scientist

Jan 2012 – Feb 2015

• Engineered pioneering remote-sensing algorithms that identified targets in satellite imagery previously undetected by existing approaches.
• Collaborated with leading scientists on oceanographic simulations, atmospheric analysis, and spectral reflectance for government intelligence applications.

NIST — NRC Postdoctoral Research Associate

Jan 2010 – Jan 2012

• Resolved long-standing reproducibility failures in e-nose technology research by designing a novel calibration and operational stabilization framework, doubling the platform’s operational window.
• Implemented novel techniques for biomolecule classification using Raman spectroscopy; designed microlithography masks and thermal-mechanical optimization for nanofabrication.
• Developed systems for Martian environment simulation and astrobiological molecule quantification; extended electronic-nose capabilities for breath-based biomarker detection.

Patents

• Computer network architecture and method for predictive analysis using lookup tables as prediction models — US12079230B1
• System for time-efficient assignment of data to ontological classes — US10691976B2

Selected Publications

• “Plasmonic-based Detection of NO₂ in a Harsh Environment.”
• “Analytical capabilities of chemiresistive microsensor arrays in a simulated Martian atmosphere.”
• “Selective, Controllable, and Reversible Aggregation of Polystyrene Latex Microspheres via DNA Hybridization.”
• “Development of Optimization Procedures for Application-Specific Chemical Sensing.”
• “Plasmonic-Based Sensing Using an Array of Au–Metal Oxide Thin Films.”

Honors & Awards

• Graduate College Valedictorian — SUNY Albany
• National Research Council Research Associateship Program Fellowship
• Department of Homeland Security Staff Award — National Biosurveillance Integration Center

Core Competencies

• Leadership & Management: team building, mentoring, strategic planning, cross-functional collaboration, AI governance, talent acquisition
• AI/ML: LLMs, agentic AI, predictive analytics, deep learning (DenseNet), active learning, document intelligence, remote sensing
• Platform & Ops: ModelOps, MLOps, data pipeline optimization, QA/QC systems, workflow automation, HIPAA compliance, CI/CD for ML