Bio
Kamarin Lee is an independent researcher working on verification and provenance in private-market data: what evidence should sit behind a number before a system is allowed to call it verified. His current work formalizes a verification standard for regulatory filing data and tests it empirically on public records.
The research grows out of two long-running threads. The first is applied measurement, beginning in marketing and consumer analytics and continuing through machine learning and media-mix modeling, work featured in Wiley’s Data Science For Dummies. The second is large-scale data and AI systems architecture, spanning private-markets investment infrastructure, enterprise automation at UiPath, and data and AI programs for Meta, T-Mobile, and other Fortune 100 organizations. LinkedIn carries the fuller professional chronology; this site centers the independent research.
Research
Research interests: Verification and provenance in financial data. Measurement under partial and self-reported records. Identity resolution across regulatory identifiers. Point-in-time correctness.
Past: Applied machine learning and media-mix modeling. Enterprise data architecture.
Capital Fields
Active, 2026
Independent research and engineering on governance as code for private markets.
A Verification Standard for Filing Data
In progress
A working specification: provenance to primary sources, per-field status, confidence labeling, versioned processing, and published error rates. Written to be implementable and auditable rather than aspirational.
Study on public regulatory filings
Registered 2026
Pre-registered study design, hypotheses, verification protocol, and analysis plan, timestamped before any study-window data was examined. Under embargo; publication to follow.
Selected Work
Applied work featured in Data Science For Dummies, Wiley
3rd ed.
Media-mix modeling in commercial practice.
Verification pipeline for regulatory filings
2026
Provenance-first ingestion with per-field status, confidence, and parser versioning; a frozen pilot codebook precedes the agreement study, so no headline agreement statistic is claimed.
Professional Activities
The selected chronology below provides context for the research; LinkedIn carries the fuller public history. The independent research program stands on its published artifacts rather than on employer affiliation.
Principal architect, enterprise data and AI
2023–
Data and AI programs for Meta, T-Mobile, and other Fortune 100 organizations across financial services, healthcare, telecommunications, and consumer sectors.
Data and AI engineering — UiPath; private-markets investment platform
2019–2023
Enterprise automation platforms; investor and offering data systems for a private-markets investment platform covering commercial real estate and private equity vehicles.
Analytics and machine learning, media and consumer
2013–2019
Measurement, modeling, and data platform work across publishing, fashion, and consumer brands.
Contact
A full curriculum vitae, including complete engagement history, is available on request.