Work & Capabilities
My background spans clinical development, statistical programming, business systems, process improvement, training, and emerging technology.
Good Gorilla is primarily where I document, build, and test ideas. I am open to selective advisory and project work, but I do not pretend every prototype is a finished product or every capability is a packaged consulting service.
Clinical and regulated systems
Reliable systems, controlled access, and processes built for regulated work.
My experience includes administering and supporting time-critical clinical trial statistical programming and reporting environments, managing access, standardizing study setup, developing controlled documentation, and helping teams remain ready for inspection.
I have worked across clinical programming delivery, GCP-aligned processes, CAPA, data standards, access governance, training, and operational support.
Areas of experience
- Clinical trial programming and reporting environments
- Study setup and standardized Linux directory structures
- User provisioning, role-based permissions, and periodic access reviews
- GCP, ALCOA+, CAPA, data integrity, and inspection readiness
- SDTM, ADaM, TLF delivery, validation, and documentation
- Vendor requirements, deliverable review, and issue resolution
Examples from My Work
✅ Administered Takeda’s time-critical TAGG clinical trial statistical programming and reporting environment, including study setup, standardized Linux directory structures, user provisioning, role-based access, and periodic access reviews.
✅ Oversaw SDTM and ADaM development and TLF delivery across global trials, with QC validation, documented outcomes, and inspection readiness.
✅ Directed CAPA work aligned with GCP and ALCOA+, conducting root-cause investigations and implementing actions that strengthened data integrity, traceability, and compliance.
✅ Authored RFPs and led vendor governance for outsourced case report form data-mapping services, reviewing deliverables and resolving issues against clinical data standards.
Knowledge platforms, training, and adoption
A system only works when people can find, understand, and use what they need.
At Takeda, I led the roadmap, governance, rollout, and adoption of a SharePoint knowledge platform used across more than 70 sites, increasing engagement by 240%.
My work has also included role-based permissions, access reviews, content standards, document-retention practices, Bloom LMS curricula, onboarding redesign, job aids, user guidance, and support for organizational change.
Areas of experience
- SharePoint roadmap, governance, administration, and adoption
- Knowledge architecture and content standards
- Role-based access and periodic access reviews
- Training-needs analysis and curriculum design
- LMS implementation and assignment management
- Onboarding, job aids, documentation, and user support
- Adoption planning, communications, and stakeholder engagement
Examples from My Work
✅ Led a Lean Six Sigma initiative that reduced SDTM delivery time by 75% and improved team satisfaction by 60%.
✅ Designed and built an automated TAGG onboarding tool that cut cycle time by 72% and increased user satisfaction by 40%.
✅ Standardized TAGG environment setup and request fulfillment, reducing turnaround from days to hours.
✅ Built an automation tool for inclusion/exclusion reporting, reducing production effort and improving consistency.
Process improvement and operational design
Find the friction. Fix the system, not just the symptom.
I’ve led process-improvement and automation efforts that reduced SDTM delivery time by 75%, cut onboarding cycle time by 72%, and made complex work more consistent and easier to support.
The goal is not automation for its own sake. It is to understand how the work actually happens, remove unnecessary effort, clarify ownership, and make the improved process sustainable.
Areas of experience
- Workflow analysis and root-cause investigation
- Lean Six Sigma and continuous improvement
- Process standardization and operating-model design
- KPI definition, dashboards, and performance visibility
- Request fulfillment and onboarding improvement
- Practical automation with appropriate controls
Examples from My Work
✅ Built an exploratory classification prototype that achieved approximately 98% precision on the evaluated data when flagging potential compliance risks in meeting records.
✅ Built a retrieval-based assistant prototype to explore document Q&A and access to procedural and compliance information.
✅ Developed Good Gorilla workflow prototypes using structured outputs, validation, and logging to explore more governed and traceable uses of AI.
✅ Used the prototypes to evaluate both potential value and limitations, including situations where AI was not yet the right solution.
AI exploration and prototyping
Start with the problem, not the model.
I use prototypes to explore where AI may provide practical value and where it may introduce more risk, complexity, or uncertainty than it solves.
That work has included classification models, retrieval-based assistants, document and knowledge workflows, structured outputs, validation, and logging. The emphasis is on learning quickly, documenting limitations, and distinguishing an interesting demonstration from something ready for real use.
Areas of exploration
- Classification and decision-support prototypes
- Retrieval-based document and knowledge assistants
- Structured outputs, validation, and traceability
- Governed workflow design
- Practical evaluation of usefulness, risk, and readiness
- Moving from prototype concepts toward maintainable tools
Examples from My Work
✅ Built an exploratory classification prototype that achieved approximately 98% precision on the evaluated data when flagging potential compliance risks in meeting records.
✅ Built a retrieval-based assistant prototype to explore document Q&A and access to procedural and compliance information.
✅ Developed Good Gorilla workflow prototypes using structured outputs, validation, and logging to explore more governed and traceable uses of AI.
✅ Used the prototypes to evaluate both potential value and limitations, including situations where AI was not yet the right solution.
How I Work
- Understand the problem first. The technology, process, or organizational structure comes after understanding what is actually getting in the way.
- Build for real use. A solution has to work for the people responsible for using, supporting, and maintaining it—not just look good in a presentation.
- Be clear about what is proven. A prototype, a successful internal solution, and a production-ready product are different things. I believe in being honest about where the work stands.
Open to the Right Conversation
I’m interested in full-time leadership roles, selective advisory work, and collaborations involving clinical development, business systems, process improvement, training and adoption, responsible AI, or the places where those areas overlap.
I’m most useful when the work is new, unclear, or crosses technical, operational, regulatory, and people boundaries.