Super Data Science: ML & AI Podcast with Jon Krohn

Jon Krohn

podcast@superdatascience.com

For verified host and producer emails, sign up to view.

Booking Overview

Super Data Science is a high-signal show for ML/AI practitioners and leaders, cutting through hype to focus on how to build real systems and careers. Guests typically share end-to-end, implementation-level insights—from data curation and workflows to deploying AI-first organizations—so it’s strong PR for technical credibility.

Metrics

Episodes: 997

Frequency: Weekly

Rating: 4.6/5.0

Estimated listeners: 1k-10k

Gender skew: Neutral

Location: USA

YouTube: 44.9k subscribers

Contact Information

podcast@superdatascience.com

For verified host and producer emails, sign up to view.

Host

Jon Krohn - Dr. Jon Krohn is the host of the Super Data Science Podcast. He covers the latest in machine learning, AI, and data career topics, interviewing prominent figures in academia and industry to transla...

Booking Intelligence

Booking Requirements

medium
Typical Credentials:  
Technical leaders and practitioners in ML/AI and data engineering; authors of relevant industry books/papers; product/engineering leaders building AI-first systems; often with demonstrable implementation experience (evaluation, deployment, workflow/agent design, RL, data curation).
Required Achievements:  
Authorship of technical or industry books (e.g., Wiley), Published research papers (e.g., NeurIPS-cited work), Built and deployed real-world AI/ML systems or evaluation frameworks, Leadership roles in ML engineering or AI product architecture

Recent Guest Discussions

Jazmia Henry - End-to-end Foundation Models For Energy; Data Wrangling/training Data; Reinforcement Learning And Inference At Scale; Tokenizers; Continuous Evaluation; Reward Hacking And Evaluation Frameworks.

Jeremy Mumford - Principles For Building Ai-first Organizations; Designing Products/processes For Human/agent/hybrid Execution; Hallucinations As Data Curation Problem; Workflow-to-agent Progression; Velocity As Competitive Advantage.

Jacob Miller - Principles For Building Ai-first Organizations; Designing For Human/agent/hybrid Execution; Hallucinations As Data Curation Problem; Workflow-to-agent Progression; Velocity As Competitive Advantage.

Recent Topics

Machine Learning, Artificial Intelligence, Data Science, Foundation Models, Data Engineering

Episodes

Here's the recent few episodes on
Super Data Science: ML & AI Podcast with Jon Krohn
:

996: TrueFoundry’s Nikunj Bajaj on How to Get $100M Returns on AI Agent Deployments

May 29, 2026

TrueFoundry co-founder and CEO Nikunj Bajaj speaks to Jon Krohn about how enterprises like Nvidia and Siemens are realizing returns of over $100 million from single agent deployments, the AI gateway architecture that makes it possible to connect, observe, and govern agents at scale, and why the familiar advice to “start small” is the wrong way to roll out AI agents inside a large organization. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/996 Interested in sp...

995: End-to-End Foundation Models for the Energy Industry, with Jazmia Henry

May 26, 2026

Jazmia Henry joins Jon Krohn to break down what it actually takes to build end-to-end foundation models for the energy industry. From wrangling decades of handwritten oil-and-gas documents into usable training data, to bespoke tokenizers, reinforcement learning, and inference at scale, Jazmia walks through every stage of the stack. Along the way she explains why reinforcement learning models are "bursty," what reward hacking is and how her Grounded Continuous Evaluation framework fixes it, an...

994: AI’s Putting Recent Grads Out of Work; Here’s How to Get Hired Anyway!

May 22, 2026

Unemployment for recent computer-science graduates now rivals rates for fine-arts and anthropology majors, and undergraduate CS enrollment fell 11% in 2025. In this Five-Minute Friday, Jon Krohn walks through the data on both sides of the debate, from Stanford research showing a 13% employment drop for young workers in AI-exposed jobs, to Federal Reserve studies finding no statistically detectable link between AI adoption and reduced hiring. Jon shares his own view on where the truth lies and...

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