AI governance, on the inside of the boardroom.

Director of Data Science with over a decade building, deploying, and governing machine learning systems at scale — paired with direct fluency in AI risk frameworks, regulatory compliance, and SEC audit processes.

Board Candidate Audit · Risk · Technology Health-Tech Focus South Florida
Niki Reid
Photo drop niki.jpg in repo root

Rare fluency across both sides of the AI question.

Niki Reid is a Director of Data Science with over a decade of experience building, deploying, and governing machine learning systems at scale at one of the world's largest technology companies. She has led teams responsible for ML model development, AI risk frameworks, regulatory compliance, and internal audit processes including SEC filing metrics — giving her rare fluency across both the technical and regulatory dimensions of AI.

Niki holds a Master's degree in Economics and brings a rigorous analytical framework to questions of risk, governance, and organizational accountability. Her work sits at the intersection of AI systems, financial compliance, and enterprise risk management — making her particularly well suited to audit, risk, and technology committees navigating the evolving landscape of AI disclosure requirements and regulatory scrutiny.

She is based in South Florida.

Where I can contribute.

AI Governance & Risk

Frameworks for responsible AI at enterprise scale — from model validation to deployment oversight to incident response.

SEC Regulatory Compliance

Firsthand experience with filing metrics, internal audit processes, and the regulatory posture public companies now require.

Machine Learning at Scale

Decade of experience shipping ML products and infrastructure at one of the largest technology companies in the world.

Enterprise Risk Frameworks

Translating model risk, data risk, and regulatory risk into language audit committees and executives can act on.

Financial Modeling & Analytics

Master's in Economics grounding, with applied experience analyzing business outcomes and operational performance.

Operator Perspective

A pragmatic understanding of what ships versus what's proposed — useful when reviewing strategy and execution.


Actively preparing for board service.


If any of this resonates, I'd love to talk.

Particularly interested in conversations with health-tech founders, boards evaluating AI exposure, and companies preparing for evolving AI disclosure requirements.