Breaking Into Data Science Careers

Ordering up AI

AI assistants are starting to do more than answer questions; they’re beginning to take action. In this episode of ChatADS, MS in Applied Data Science alumna Hailey Kleban joins Daniel to discuss Resy’s new integration with Claude, the future of agentic AI in restaurant booking, and the real-world challenges of balancing automation, security, and human oversight.

Is It Possible to Predict the Crypto Market Using AI?

Students from the University of Chicago's MS in Applied Data Science program share how they built a real-time crypto trading system for their Real Time Intelligent Systems class, and what they learned about volatility, model performance, and decision-making in fast-moving markets. Explore the project here on GitHub: https://github.com/shreyast36/nexus-trading

Breaking Into Data Science Careers

In this episode of ChatADS, Daniel chats with Ben Wiebers from Career Services to break down what it actually takes to land a role in data science. Drawing on his experience as a corporate recruiter, Ben shares what hiring teams are really looking for, common mistakes candidates make, and how to stand out in a competitive job market. From building a strong narrative to navigating rejections and tailoring your applications, this episode is packed with practical advice for current students and early-career professionals.

Waymo Comes to Chicago

In this episode of ChatADS, Professor Arnab Bose shares his experience working on early autonomous vehicle systems and reflects on how the field has evolved with modern AI. With Waymo preparing to enter Chicago, the conversation explores: * The technical foundations of autonomous driving * Real-world deployment challenges in urban environments (hello Chicago snow) * Ethical and regulatory considerations This discussion offers a technical and practical look at the future of autonomous cars.

Keeping Pace with AI

In this episode of ChatADS, we sit down with MS in Applied Data Science alum Nathan Rickert, now a data scientist at Thrivent, to explore how AI is being applied in the financial space. Nathan shares how his team is building across predictive, generative, and agentic AI, and why understanding the difference between them matters in practice. The conversation dives into how organizations are using AI to drive efficiency while keeping humans at the center of decision-making, especially in regulated industries. We also discuss one of the biggest challenges in today’s AI landscape: not just learning new tools, but knowing which ones actually matter. From stakeholder collaboration to ethical considerations, this episode offers a grounded look at what it means to work in AI today.