CompoundIQ
503B facility lifecycle tracking from a single FDA spreadsheet, modeled into history you can monitor, query, and trust.
View case study
Principal Pharmacy Intelligence & Optimization Engineer
Decision-grade intelligence for compound and sterile drug markets
I design intelligence and optimization systems for compound drug procurement: where pricing, compliance, and operations intersect.
Focus Areas
What I work on
Market Intelligence
- Supplier and pharmacy landscape insights
- Licensing, accreditation, and capability intelligence
- Competitive positioning and benchmarking
Optimization & Strategy
- Drug sourcing and formulary optimization
- Pricing and contract tradeoff modeling
- Substitution and shortage response planning
Decision Systems
- Ingesting messy regulatory, pricing, and operational data
- Optimization under real-world constraints
- Decision support for sourcing and risk management
Business Intelligence
- Build Data Warehouses, Pipelines & Integrations
- Dimensional modeling & metrics
- Interactive dashboards & data apps
Portfolio
Selected Systems & Case Studies
California Sterile License Study (2024)
Turning a fragmented licensing portal into a searchable, enriched directory and market analysis of California sterile compounding capacity.
View case studySterileCalc
A lightweight procurement calculator for sterile drugs that turns messy dosing, vial, and compliance rules into fast, defensible purchasing math.
View case studyMedSupplyPro
A privacy-first, offline Android application for medical inventory tracking built for solo providers and mobile practices.
View case studyData Doctor
A privacy-first, browser-based tool for diagnosing and fixing spreadsheet data quality—no accounts, no uploads to third parties.
View case studyThe Challenge
Why this problem doesn't yield to dashboards alone
Compound pharmacy data is fragmented, overwritten, and often released in formats that resist automation. Pricing behaves non-linearly due to aggregation agreements. Regulatory signals are buried in PDFs and portals with no historical continuity.
Most analytics approaches fail because they assume clean inputs and stable definitions. I design systems that assume the opposite.
Philosophy
How I Think
Data Quality and Validation
In regulated markets, data quality isn't a nice-to-have—it's the foundation of defensible decisions. I build validation into every layer of a system, not as an afterthought but as a core design principle.
This means explicit data contracts, automated anomaly detection, and clear lineage from source to output.
Guardrails for AI and Machine Learning
Machine learning has genuine applications in pharmacy analytics—demand forecasting, anomaly detection, entity resolution. But ML models in regulated environments require guardrails that most implementations lack.
I'm skeptical of black-box predictions for high-stakes decisions. Models should be interpretable and validated against domain expertise.
Human-in-the-Loop Systems
The best decision systems don't replace human judgment—they augment it. In procurement and compliance, context matters in ways that algorithms can't fully capture.
I design systems that surface relevant information, flag anomalies, and suggest actions, but preserve space for expert override.
Right-Sized Architecture
Not every problem needs a distributed data platform. Some of the most effective decision systems I've built run on surprisingly modest infrastructure because the architecture was matched to the actual requirements.
Complexity should be earned, not assumed.
Skepticism of Black-Box Systems
In healthcare procurement, decisions have real consequences—patient safety, regulatory compliance, financial exposure. Systems that can't explain their recommendations aren't trustworthy enough for these contexts.
I build systems where every output can be traced to its inputs and where assumptions are explicit.
Principles
How I approach analytics in regulated markets
Metrics before dashboards
Validation before prediction
Interpretability over novelty
Automation with accountability
Optimization requires domain judgment, not just math
If you're working on procurement strategy, supplier risk, or market intelligence in compound or sterile drugs, I'm always open to thoughtful conversations and can be reached on LinkedIn.