DataFramed

Career goals in data have shifted quickly. Data scientist was long the default target, but AI engineer has appeared from almost nowhere, and companies are moving training toward AI literacy, agents, and prompt engineering. Certifications are becoming a common way to prove these skills, while many people wonder whether learning to code still matters when AI can write it for them. What are people choosing to learn right now? Which fundamentals still hold up? How do you prepare for a certification exam? And is the fear of AI replacing technical roles showing up in what people actually study?
Pieter Moors is the Lead Data Scientist at DataCamp, where he works with the company's learner data to understand how people learn and how that behavior changes over time. He is part of DataCamp's new Data Insights program, which shares findings from that data with the world every couple of weeks.
In the episode, Richie and Pieter explore what DataCamp's own learner data reveals, when enterprise and individual learners study, how the World Cup disrupted study time, why short regular sessions and streaks build habits, how AI tutors relate to certification pass rates, the shift from data scientist to AI engineer, and much more.
Links Mentioned in the Show:
Connect with Pieter
DataCamp AI Tutor
DataCamp Certifications
Introduction to Python course
DataCamp AI Assistant
DataCamp mobile app
DataCamp subreddit
AI-Native Course: Intro to AI for Work
Related Episode: The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills

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Most hiring decisions still come down to gut feel, and gut feel is full of blind spots — a favorite school, a firm handshake, the type of coffee someone carries into the room. As AI reshapes recruiting, the industry is being forced to ask whether decisions can be made on evidence instead of instinct. That means defining what actually predicts success in a role, testing every candidate against the same criteria, and being able to show the reasoning behind a hiring call rather than just a hunch. For anyone building a team, the question isn't whether bias exists — it's whether your process is built to catch it before it shapes who gets hired.
Daniel Chait is the co-founder and CEO of Greenhouse, the hiring software used by tens of thousands of companies to run structured, data-driven hiring processes. Ophir Samson is Greenhouse's Head of Voice AI, leading the team following Greenhouse's acquisition of Ezra AI Labs, the voice AI interviewing company he founded.
In the episode, Richie, Daniel, and Ophir explore the flood of AI-generated resumes drowning both job seekers and recruiters, how Voice AI interviews let candidates showcase themselves beyond a resume, structured hiring and scorecards for fairer decisions, bias audits and anti-cheat safeguards, EU AI Act compliance, and measuring success through pass-through rates and time savings, and much more.
Links Mentioned in the Show:
• Zapier <> Greenhouse: Conversational Application Voice AI
• Paradigm Strategy
• Anthropic
• Pipecat on GitHub
• Daily
• Are Emily and Greg More Employable Than Lakisha and Jamal? (AEA)
• Connect with Daniel: LinkedIn
• Connect with Ophir: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: How to Have a Machine Learning Career in 2026
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A job title can stay the same while the work underneath it changes completely. That idea ran through RADAR 11x, our online conference, and this recap keeps the best moments from four of the day's eight sessions. You will hear how to sort your tasks between you and AI, which durable skills hold their value, why pilots stall before production, and how software teams work with coding agents. Missed RADAR? Start here. Which tasks should AI take over? Which skills deserve your next hour of learning?
Featured in this recap: Ben Zweig, CEO at Revelio Labs; Nicole Immorlica, Professor at Yale University and Senior Principal Researcher at Microsoft; Margaret Beier, Professor at Rice University; Matt Jones, EVP of Strategy at Cielo Talent; Krishnan Hariharan, CTO and VP of Engineering for Forge at Honeywell; Satesh Sonti, Principal Specialist Solutions Architect at AWS; Laurent Gil, President and co-founder at Cast AI; Joao Moura, CEO at CrewAI; Ali Arsanjani, Senior Director of Applied AI Engineering at Google; and DataCamp co-founders Jonathan Cornelissen (CEO) and Martijn Theuwissen (COO).
In the episode, Richie rounds up the best moments from RADAR 11x, exploring whether AI will take your job, the durable skills worth building, why AI pilots stall, cutting token costs, how software teams work with agents, the future of the data analyst role, and much more.
Links Mentioned in the Show:
• We Are Not Machines (Sarah O'Connor)
• Jevons paradox
• AI-Driven Development Life Cycle (AI-DLC) (AWS)
• The State of AI Careers 2026 (DataCamp)
• Watch on demand: Humans On The Loop
• Watch on demand: Don't Waste Your Time on AI Pilots
• Watch on demand: The New Developer Workflow
• Watch on demand: Closing and AMA
• RADAR 11x event page
• AI-Native Course: Intro to AI for Work
• Related Episode: Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs
New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

AI agents are moving from demos into production, but many teams run into the same wall: responses that take too long and cost too much. As models grow bigger and reason through more steps before answering, every chained agent adds delay, and that adds up fast at scale. For data and AI professionals, this raises a practical question: how do you pick a model and an inference platform that keeps a multi-agent workflow fast without blowing the budget? And underneath that choice sits a bigger one — how much of the AI stack, from chips to software, should a company actually control?
Sumti Jairath is Chief Architect at SambaNova Systems, where he's focused on machine learning, big data analytics, and software-defined hardware since 2017. Before that, he spent close to eight years as a Senior Hardware Architect at Oracle working on hardware acceleration for machine learning and data analytics, and earlier held processor and server design roles at Hewlett Packard and Sun Microsystems. He holds a B.Tech in Electronics and Communication Engineering from the National Institute of Technology, Kurukshetra.
In the episode, Richie and Sumti explore why AI agents are slow and expensive, the architecture behind faster inference, choosing the right model and platform for a task, open-source models and sovereign AI, the full AI infrastructure stack, power efficiency, the AI data center debate, career paths in AI infrastructure, and much more.
Links Mentioned in the Show:
• NVIDIA's Groq acquisition, announced around GTC
• Chris Olah, Anthropic
• Anthropic and OpenAI's coding-agent-driven revenue growth
• Ollama
• Apple Mac mini
• SambaNova careers
• Connect with Sumti
• AI-Native Course: Intro to AI for Work
• Related Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAI
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As agents take over more of the actual coding, the nature of technical work is shifting from producing software to judging it. Engineers increasingly spend their time specifying what should be built and then checking whether an agent's output actually solved the problem, rather than writing every line themselves. That changes what skills matter for a career in data and software: problem-solving and evaluation start to outweigh knowing today's specific tools or syntax. It also raises a harder question for teams — if agents can generate work this fast, how do you know which of it is actually worth shipping?
Ledion Bitincka is Co-Founder and CTO of Cribl, where he leads the engineering organization with a first-principles approach to product delivery. Before Cribl, he was an Advanced Development Architect at Splunk, where he worked on Search-Time Schema, Hunk, and SmartStore, and before that founded Triangulus Communications. Nikhil Mungel is Head of AI R&D at Cribl, based in San Francisco, with over 15 years building distributed systems and AI teams at companies including Substack, Splunk, and ThoughtWorks — he now leads teams building LLM-powered systems for IT and security data.
In the episode, Richie, Ledion, and Nikhil explore AI agent disasters and cost shocks, software telemetry fundamentals, using agents to analyze telemetry, AI-powered software factories, the shift from knowledge work to judgment work, skills for the agentic era, avoiding runaway AI spend, and measuring product value through growth metrics, and much more.
Links Mentioned in the Show:
• Jocko Willink's book, Leadership Strategy and Tactics: A Field Manual
• Cribl
• Cribl AI (Copilot)
• Claude Code
• AWS Graviton
• NVIDIA
• Connect with Ledion: LinkedIn
• Connect with Nikhil: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It)
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Learn on the go using the DataCamp mobile app
Empower your business with world-class data and AI skills with DataCamp for business