I connect what AI can do with what pharma must do.
25 years across quality, manufacturing, labs, and R&D — from Pfizer's biologics floor to Sanofi's process engineering to leading AI and digital strategy at Takeda. Now helping pharmaceutical organizations harness agentic AI without breaking what's regulated.
25
Years Experience
How I Help
Three problems I solve that most consultants can't — because they've only lived on one side.
AI & Digital Transformation in Regulated Environments
Your organization is under pressure to adopt AI — including agentic AI — but your quality and validation teams see risk everywhere they look. Meanwhile, your IT team doesn't fully understand GxP, and your quality team doesn't speak data architecture.
I've deployed machine learning systems that prevented $18M in product losses, built enterprise data platforms adopted across 80% of manufacturing sites, and led the technical architecture for AI-powered quality risk management. I do this work inside the regulatory reality, not around it.
What you get: An implementation roadmap that satisfies both your innovation mandate and your regulatory obligations — built by someone who's done it in production, not in a slide deck.
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Quality Strategy & Operational Excellence
Your quality organization is stuck in reactive mode — chasing deviations, managing audit findings, and struggling to demonstrate strategic value. Projects compete for resources without clear prioritization, roles blur across functions, and good initiatives stall because there's no governance framework to support them.
I've led strategy for a 100+ person quality organization, established digital governance boards that prioritized portfolios delivering $40M+ in business value, and designed project excellence frameworks from the ground up — including mentorship programs, digital knowledge hubs, and capability-building curricula.
What you get: A quality function that operates as a strategic asset, not a cost center — with clear governance, measurable outcomes, and frameworks that sustain themselves after I leave.
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Data Infrastructure & Digital Maturity
You have dashboards, but no unified visibility. You have data, but it's siloed across LIMS, SAP, MES, and paper records. Your team licenses five analytics platforms when you need a rational framework for two. And your digital maturity varies wildly across sites with no consistent way to measure or improve it.
I've built enterprise data lakes from zero to 80% adoption, created digital control rooms monitoring 200+ KPIs, grown self-sustaining communities of 40+ dashboard developers, and designed site maturity assessments that gave executive leadership a data-driven view of capability for the first time.
What you get: Connected data infrastructure, rationalized platforms, and a maturity roadmap that turns scattered digital efforts into a coherent strategy — with the organizational adoption to make it stick.
Book a Conversation →Selected Work
Results from 25 years across pharma manufacturing, quality, and digital transformation. Details anonymized to respect confidentiality.
Preventing $18M in Product Losses Through Real-Time Analytics
Contamination events in biologic manufacturing were causing significant product losses. Detection was reactive — problems were identified after batches were already compromised.
Approach
Partnered with manufacturing leadership to identify contamination detection as the highest-value use case. Deployed multivariate statistical process monitoring integrated with the existing process historian, focusing on a targeted solution with minimal infrastructure disruption.
Result
Prevented several contamination events, saving approximately $18M in potential product losses. Established a replicable template for high-impact, low-disruption analytics deployments and built credibility that unlocked investment in broader data initiatives.
Automating a Critical GxP Method — Zero Deviations, 50% Faster Transfer
A critical tech transfer of the company's highest-revenue product required automating a complex analytical method involving 36 manual steps across three separate systems. The existing process carried significant compliance risk.
Approach
Leveraged an existing validated data science platform to implement R-based automation within a 21 CFR Part 11 compliant environment. Used rapid prototyping with the QC team while maintaining full validation requirements. Conducted thorough comparability testing and secured both local and global QA approval.
Result
Cut the transfer timeline by 50% and met the tech transfer deadline. Automated 30 of the 36 manual steps. Zero deviations in subsequent years of operation. Avoided a drug shortage for patients.
From Zero to 80% Data Lake Adoption Across Manufacturing
A modern cloud-based data platform existed but had near-zero adoption after six months. Data remained siloed across manufacturing sites, and approximately 1,000 potential data consumers were unaware of or intimidated by the platform.
Approach
Established a digital governance board to align the platform with the strategic roadmap. Took a user-centered adoption approach: conducted communities of practice that revealed users were intimidated by SQL and unfamiliar tools, then implemented use-case-specific workshops, simplified data access layers, and integration with familiar BI tools.
Result
Adoption rose from 0% to 50% within two weeks of the revised approach, eventually reaching 80% across manufacturing sites within 18 months. The model was replicated across three additional sites. Users began creating their own dashboards, and the technical team's role shifted from owners to enablers.
Preventing $10M in Supply Chain Disruptions Through Integrated Data
During a major global supply chain crisis, rapid decisions about raw material replacements were needed to avoid manufacturing shutdowns. The data required was scattered across SAP ERP, vendor publications, production records, and paper archives.
Approach
Had previously built a unique platform that tracked raw materials data globally, integrating specifications, batch data, and experimental results across internal and external sources. When the crisis hit, embedded a team member in the strategic materials group and enabled rapid access to the critical data needed for material replacement decisions.
Result
Prevented approximately $10M in costs including chromatography resin lots, idle facility expenses, and 13,000 saved work hours. Demonstrated the strategic value of integrated data platforms during a real business crisis.
About
I started on the manufacturing floor at Pfizer — tech transfer for monoclonal antibodies, 12,000-liter bioreactors, learning how pharma production actually works. At Sanofi, I moved into process engineering and discovered that data was the fastest lever for solving manufacturing problems. At Takeda, I've spent eight years progressively leading digital transformation — from production systems to data science leadership to quality strategy for a 138-person organization.
That arc matters because the biggest barrier to AI in pharma isn't the technology. It's the gap between the people building the models and the people who understand what 21 CFR Part 11 actually requires on a Tuesday morning. I've lived on both sides, which means I don't just design solutions — I design solutions that survive validation, audits, and the realities of regulated manufacturing.
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If you're navigating AI adoption in a regulated environment, building digital maturity across manufacturing sites, or rethinking how your quality organization creates strategic value — I'd welcome the conversation.
Contact Information
Location
Available for global consulting