04
ActiveClinical Trial Diversity Studio
A redesign of the ClinicalTrials.gov experience with AI assisted discovery and trial diversity workflows.
Live ClinicalTrials.gov dataPlain-language searchSemantic rerankingProfessional and public UX
Senior Product Manager · Data & AI Products · Greater New York City Area
I turn complex information into products people can use.
Senior Product Manager with 7+ years across product, data, and analytics. I own the path from customer discovery and roadmap decisions through cross-functional delivery, adoption, and commercialization—using data and practical AI where they make the workflow better.
Experience
Johnson & Johnson
September 2026 to Present
September 2026 to PresentEngagement Manager, Patient Engagement
JANUS
Originally scheduled to begin in March 2026. Start moved to September 2026 due to visa processing.
Syneos Health
June 2021 to November 2025
June 2021 to November 2025Global biopharmaceutical solutions spanning clinical development and commercialization.
Symphony Health
June 2019 to May 2021
June 2019 to May 2021Healthcare data and analytics, now part of HealthVerity.
Most recent impact
- 24 hours to 2 to 3 hours
- Proposal data turnaround
- 65+
- Monthly active users across 5 to 6 teams
- 20+*
- Enterprise engagements supported
- Approximately $10M*
- Revenue associated with engagements using the platform
* Engagement count and associated revenue cover the 2024–2025 period.
Current role
Engagement Manager
Patient Engagement, Johnson & Johnson
JANUS
Current focus
- Patient engagement
- Healthcare data products
- AI enabled workflows
- Product and engagement strategy
Primary target
Senior Product Manager
Secondary targets
- Data and AI products
- Enterprise platforms
- Product Strategy Consultant
- Technical Product Manager, Data Platforms
- Healthcare and life-sciences products
What I own
Product leadership grounded in users, evidence, and delivery.
Product leadership
- Product strategy
- Customer discovery
- Roadmap ownership
- Prioritization
- Requirements
- UX
- Adoption
- Commercialization
- Executive communication
AI and data products
- AI-assisted workflows
- Retrieval-augmented generation (RAG)
- AI evaluation
- Data platforms
- Self-service analytics
- Product analytics
Healthcare and commercial experience
Claims · EHR · HCP and HCO data · Veeva CRM · Segmentation · Targeting · Healthcare vendor evaluation · Commercial analytics
Technical foundation
SQL · Python · Tableau · Power BI · Jira · Confluence · Figma
Selected work
Selected problems, told properly.
01
Commercial productEnterprise healthcare data platform
Led the evolution of a fragmented healthcare data environment into a self service platform supporting cohort development, commercial planning, targeting, and enterprise client delivery.
- 65+ monthly active users
- 20+ engagements*
- 24 hours to 2 to 3 hours
* Engagements and associated revenue cover the 2024–2025 period.
View the work02
Professional case studyHealthcare data vendor strategy
Evaluated healthcare data partners across coverage, freshness, match rates, usability, cost, and strategic fit to inform a major platform and vendor decision.
Data strategyVendor evaluationBuild versus buyCommercial analytics
- ~25–50% projected data spend reduction
- ~30% usable coverage expansion
Approximate ranges from the vendor evaluation.
See the project work03
Pilots and deployed featuresAI product decisions
A collection of AI product experiments covering knowledge retrieval, analyst routing, summarization, code recommendation, and natural language data access, including features that were deliberately not moved into production.
RAGAI evaluationHuman reviewProduct judgment
- 5 to 6 pilot users
- Approximately 50 to 90 queries per week
- ~60% estimated assignment time reduction*
- ~30 to 45 min reduced to 15 to 20 min in supported interpretation workflows
* Routing figure is an estimate from POC testing. Retrieval stayed a pilot; Text to SQL was not advanced to production.
See the project workHow I work
I start with the workflow, then question what the data can support.
My approach comes down to four habits: understand the workflow first, question what the data can support, make complex work easier to do, and connect shared capabilities to different needs.
How I workProduct environments
Enterprise platforms · AI-enabled workflows · Data products · Self-service analytics
Domain experience
Healthcare · Life sciences · Commercial analytics
Let's talk about the problem, first.
Happy to walk through any of the case studies in detail, including the parts that didn't work.