High Signal: Data Science | Career | AI
Hugo Bowne-Anderson
hugobowne@gmail.com
Booking Overview
High Signal is a data science and AI career-focused show that turns complex technical and org-level challenges into practical guidance. It’s especially useful for leaders and practitioners who want to understand how AI changes data work, governance, and decision-making—so they can position themselves (and their teams) for impact.
Metrics
38 episodes, Irregular, 5.0 rating
<1k, Male, USA
30s Ad: 11 - 14, 60s Ad: 13 - 16, CPM Category: Technology
Contact Information
hugobowne@gmail.com
Description
Welcome to High Signal, the podcast for data science, AI, and machine learning professionals.
High Signal brings you the best from the best in data science, machine learning, and AI. Hosted by Hugo Bowne-Anderson and produced by Delphina, each episode features deep conversations with leading experts, such as Michael Jordan (UC Berkeley), Andrew Gelman (Columbia) and Chiara Farranato (HBS).
Join us for practical insights from the best to help you advance your career and make an impact in these rapidly evolving fields.
More on our website: https://high-signal.delphina.ai/
Production Team
Host
Hugo Bowne-Anderson
Categories
Technology, Business, Careers, Science
Booking Intelligence
Booking Requirements
medium
Typical Credentials:
Senior practitioners and public experts in data science/ML/AI (e.g., distinguished engineers, data/AI transformation leaders, enterprise architects), often with strong communication credentials such as authorship, leadership roles, or well-known research/academic standing.
Required Achievements:
Executive or distinguished engineering leadership in AI/data transformation, Published books or widely recognized frameworks for data literacy/AI skills, Academic or research reputation in ML/AI (e.g., university-affiliated experts), Founded/led AI/data platforms or consultancies
Recent Guest Discussions
Noah Bruegmann - Ai’s Impact On Data Culture, Moving Data From Cost Center To Value Center, Reporting/verification Practices, Problem Framing, And Governance/verification In Llm-era Workflows., Jordan Morrow - Shift From Data-driven To Ai-enabled Organizations, Data Literacy/skills, Engineered Intelligence (human+machine), And Risks Of Shadow Ai., Jeremy Hermann - Ai-era Data Science Workflow Verification, Integration Into Enterprise/brownfield Environments, And Practical Constraints Between AI Hype And Operational Reality., Andrs Bucchi - Enterprise Ai/data Science Architecture Considerations In The Shift From Dashboards To Conversational/vibe-coding Workflows; Operationalization And Correctness Challenges., Dawn Woodard - Architecture Of Data Science/analytics For AI Era; Verification Vs Generation, Source-of-truth Challenges, And Security Patterns For Agents.
Recent Topics
Data Science, Machine Learning, Artificial Intelligence, Career, Data Governance, Data Literacy, Analytics, Organizational Change, Model Evaluation, Enterprise Ai
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