Director of Data Science with over a decade building, deploying, and governing machine learning systems at scale at Meta, with direct fluency in AI risk frameworks, regulatory compliance, and SEC audit processes.
Niki's work sits where AI systems meet regulatory scrutiny. Over a decade at Meta, she has shipped ML products at scale and built the internal processes that make them defensible: model risk registers, documentation standards, audit trails, and the SEC filing metrics that public companies are now expected to produce.
Her training in Economics, paired with direct operating experience, gives her a rigorous lens on governance and accountability. She is well suited to audit, risk, and technology committees evaluating how AI gets built, how it fails, and how it shows up in disclosures and board reporting.
She is based in South Florida.
Frameworks for responsible AI at enterprise scale: model validation, deployment oversight, and incident response.
Firsthand experience with filing metrics, internal audit processes, and the regulatory posture public companies now require.
Decade of experience shipping ML products and infrastructure at one of the largest technology companies in the world.
Building the internal controls that make AI deployments defensible: model risk registers, documentation standards, escalation paths, and the audit trail a regulator would want to see.
The board-facing side of the same question: reading what a CTO is presenting, flagging AI-related disclosure obligations, and knowing what a committee can and cannot ask of management.
Boards benefit from someone who knows what execution actually looks like, not just what gets presented. I've shipped at scale and can stress-test the CTO's deck.
Currently in a period of focused study and preparation: pursuing NACD certification and completing Women on Boards. Email is the best way to reach me.