Built for high-stakes, regulated environments.
I build data and AI products that people actually use, in environments where being wrong has real
consequences.
My path started in Accra, Ghana, where I spent five years as a solutions architect in telecom, designing
systems for fraud detection, KYC compliance, and millions of subscribers. That work taught me something that
most data professionals never fully learn: reliability and consequence are design inputs, not afterthoughts.
A provisioning error at scale isn't a bug; it's a regulatory incident.
I then earned my Master's at Carnegie Mellon (Highest Distinction, 2023), where I was awarded the
ISM Most Outstanding Teaching Assistant, teaching Distributed Systems, Database Management,
and Data Visualization while consulting with Estée Lauder, Yum Brands, and building a public workforce
analytics tool with the Block Center for Technology & Society.
Today I lead the analytics and AI product work behind $170M+ in annual public-program
decisions in Allegheny County. I lead work related to data analytics and the
operationalization of two major predictive-risk models, including the self-service platforms program staff
use day to day, and I govern it all under HIPAA and FERPA.
My work sits at the intersection of three things most teams keep siloed: deep technical
build (ETL, data warehousing, cloud, ML), product ownership (scoping,
prioritization, adoption), and regulated-domain judgment (the ability to operate
responsibly with data that affects real lives).
I'm currently targeting AI / Technical Product Manager roles in organizations where this
combination is uniquely valuable. (e.g. Tech, Fintech, Health, Public Sector)
