Leonid Rosenfeld

The enterprises with the most to gain from AI are often the slowest to deploy it, and in regulated industries, that caution is rational — the risk is real. Data quality failures don't just break pipelines in financial services — they break compliance, erode model trust, and end careers. The bar for "production-ready" AI is fundamentally different when your data touches trades, audits, or PII. In this session, Barr Moses, CEO of Monte Carlo, joins Lenny Rosenfeld, VP of Data Science & Software Engineering at Nasdaq, for a conversation on what it takes to get trusted AI into production in financial services. They'll cover AI observability, data lineage, and governance frameworks built for regulators — sharing the failures they've seen, the patterns that work, and what they'd do differently.

As more organizations feel the pressure to deploy AI in production, their leaders face a critical challenge: moving faster without sacrificing reliability. This panel brings together data + AI leaders to discuss how a proactive approach to data foundations empowers their teams to deliver reliable AI at scale. This discussion will explore the relationship between strategy, structure, and tooling to drive business impact and work smarter.

In this talk, we will discuss the lessons learned and future vision of transforming two business units to a modern financial data platform at Nasdaq. We'll highlight the transition from disjointed systems to a unified platform using Databricks. Our target audience includes financial engineers, data architects and technical leaders. The agenda covers challenges of legacy systems, reasons for choosing Databricks and key architectural decisions.