As the Chief Analytics Officer for New York City, I witnessed firsthand how data science and AI can transform public service delivery while navigating the unique challenges of government implementation. This talk will share real-world examples of successful data science initiatives in the government context, from predictive analytics for fire department risk modeling to machine learning models that improve social service targeting.

However, government data science isn't just about technical skill—it's about accountability, equity, and transparency. I'll discuss critical pitfalls including algorithmic bias, privacy concerns, and the importance of explainable AI in public decision-making.

We'll explore how traditional data science skills must be adapted for the public sector context, where stakeholders include not just internal teams but taxpayers, elected officials, and community advocates.

Whether you're a data scientist considering public service or a government professional seeking to leverage analytics, this session will provide practical insights into building data capacity that serves the public interest while maintaining democratic values and citizen trust.