Podcast · Tech & Science

DataFramed

New episodes: Weekly
Typical length: 49 min
Latest: 7 Oct 2026
Language: English

Latest episode

7 Oct 2026 · 48 minNew

#380 The Best Moments from RADAR 11x: AI, Jobs, Agents and Skills

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…

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Earlier episodes 29 most recent

28 Sep 2026 · 48 min

#379 The Secrets of Deploying AI in Production | Sumti Jairath, Chief Architect at SambaNova

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…
21 Sep 2026 · 51 min

#378 The Data Engine for AI with Ledion Bitincka, CTO at Cribl & Nikhil Mungel, Head of AI R&D at Cribl

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…
14 Sep 2026 · 38 min

#377 The Algorithm for Hypergrowth with Jon McNeill, CEO at DVx Ventures & Former President at Tesla

Hypergrowth companies rarely scale on strategy alone — they scale on how fast decisions get made and how much risk employees feel safe taking. A recurring idea in high-growth environments is treating decisions differently depending on whether they're reversible, and rewarding people for making an impact rather than simply avoiding mistakes. For managers and individual contributors alike, that shift changes what "good work" looks like day to day. Which of your team's decisions actually need a leader's sign-off, and which ones would move faster if people just tried something and adjusted? Jon…
7 Sep 2026 · 42 min

#376 Rethinking the Data Stack in the age of AI with Tristan Handy, President of Fivetran + dbt Labs

Across the data and AI industry, infrastructure that once served dashboards and human analysts is being rebuilt to serve autonomous agents instead. That shift changes what "AI-ready data" actually means, pushing teams to rethink documentation, governance, and the semantic layer so agents pull consistent, trusted definitions rather than guessing. Day to day, this shows up as pressure to clean up gold-layer tables, eliminate duplicate metrics, and formalize business logic that used to live only in someone's head. It raises real questions: how clean does data need to be before agents can safely…
31 Aug 2026 · 47 min

#375 Is Math The Key to Better Coding AI? With Tudor Achim, CEO at Harmonic

AI capability in mathematics jumped before most people noticed, tackling Olympiad-level problems and unsolved research questions that had resisted attack for years. But the pattern of where AI succeeds and where it stalls is uneven and worth understanding. It's much better at grinding through cases to disprove something than at constructing an elegant, original proof. Anyone working with AI in a technical field runs into this same asymmetry. Where exactly is the boundary between tasks AI can already do reliably and ones that still need human judgment and creativity? Tudor Achim is the…
24 Aug 2026 · 46 min

#374 How to Thrive in a World of Continuous Transformation | Phil Le Brun and Jana Werner, Executives in Residence at AWS

Technology is now moving faster than the organizations trying to adopt it. A model can be tested in an afternoon, but the approval to test it can take half a year, and that gap is where most transformation budgets quietly disappear. The structures that made companies safe and predictable — layers of sign-off, centralized control, standardized processes — were built for a world where getting things wrong was expensive. That world is gone. So what actually has to change inside a company for AI to deliver value? Which habits are holding things up? And where do you start when everything needs…
17 Aug 2026 · 52 min

#373 What Do Your Colleagues Do All Day? (The Value of Institutional Knowledge & AI for Process Reengineering) | Jennifer Smith, CEO at Scribe

Four years into the AI boom, headlines still promise agents that will run entire departments, yet most companies can't point to the transformation they were sold. The gap isn't intelligence — today's models are remarkably capable — it's context: no model arrives knowing how your company actually gets things done. For anyone tasked with deploying AI at work, this raises pressing questions. What does it take to turn generic intelligence into something that understands your specific operations? And why do so many well-funded AI initiatives stall before they ever reach production? Jennifer Smith…
10 Aug 2026 · 49 min

#372 Bulletproof Large Scale Data Science with Srini Raghavan, Chief Product Officer at Freshworks

Software buying decisions used to be made once, by someone far removed from the people actually using the tool. That model is breaking down. Teams now expect software to work out of the box, without weeks of setup, integration, and configuration before anyone sees value. At the same time, a growing share of "users" aren't people at all — they're AI agents calling the same systems through APIs and chat interfaces. That raises a set of questions worth sitting with: what happens to user experience when the user isn't human? Can personalization and simplicity coexist, or is one always traded for…
3 Aug 2026 · 40 min

#371 The Real Reason Your Product Team Needs a Feedback Loop with Todd Olson, CEO at Pendo

Software teams are shipping faster than ever, but speed hasn't solved the oldest problem in the industry: most software still isn't very good. AI coding tools have lowered the barrier to building something, yet they haven't lowered the barrier to building something worth using. As more people who aren't trained software creators start shipping products, a new question is forming across product, design, and engineering teams: if AI can build almost anything, how do you make sure it builds the right thing, and builds it well? Todd Olson is co-founder and CEO of Pendo, the product experience…
27 Jul 2026 · 44 min

#370 Failure is Data (and Other Career Advice) | Todd Dewett, Leadership Author & Speaker

As AI takes over more technical and routine work, the skills that set data and AI professionals apart are shifting. Raw technical ability and a high IQ still matter, but they are becoming table stakes as tools get more capable and teams get smarter. What increasingly separates people is harder to automate: communication, self-awareness, authenticity, and the ability to keep learning through failure. For anyone building a career in this space, that raises real questions. Which skills are actually worth investing in now? What holds up as AI advances? And how do you keep growing once you have…
20 Jul 2026 · 49 min

#369 How to Become a Top Business Intelligence Analyst | Helen Wall, Founder at Helen Data Design & Microsoft Influencer

Business intelligence has never been only about building charts and writing queries. Most of the work that makes a report trustworthy happens below the surface — in the data models, documentation, and stakeholder conversations that users never see. For analysts, technical skill is just the starting point; understanding what the business actually needs, and why a number exists, matters just as much. So what really separates a competent analyst from a great one? How do you build something that answers the right question, not just any question? And which skills are worth investing in first?…
13 Jul 2026 · 51 min

#368 AI Agents Are Now Your Database's Main User | Reynold Xin, Co-Founder at Databricks

For forty years, the rule held that transactional and analytical databases had to be separate systems, connected by fragile pipelines that move data from one to the other. That assumption is now being questioned. As AI agents start generating the majority of database activity, the old architecture is being redesigned around speed, scale, and a single copy of governed data. For anyone who works with data day to day, this raises practical questions. Do you still need separate systems for live and historical data? What happens to the pipelines you maintain? And how does your stack change when…
6 Jul 2026 · 52 min

#367 Don't Build on Jell-O: How to Make Agentic AI Reliable with Dan Klein, CTO at Scaled Cognition

Across the AI industry, capability has exploded while trustworthiness has lagged badly behind. The same technology that writes fluent prose can invent a refund policy that was never real, and most of those errors are subtle enough that no one notices. As more teams hand high-stakes work to AI — in banking, healthcare, customer service — the cost of confident mistakes adds up fast. So how common are hallucinations, really? Can chaining models together or adding humans to the loop fix it? And is reliability something you can design into a system from the start? Dan Klein is the CTO and…
29 Jun 2026 · 49 min

#366 Can AI Agents Outperform a Data Scientist? | James Zou, Professor at Stanford University

AI agents are no longer limited to automating routine tasks like customer support or report generation. Research labs and pharmaceutical companies are beginning to deploy teams of specialist AI agents capable of designing experiments, analyzing data, and proposing new hypotheses — in some cases producing results that outperform human experts. For data scientists and researchers, this raises urgent questions: Where do AI agents excel in scientific workflows today, and where do they fall short? How do you build an agent that can genuinely innovate rather than just replicate what's already been…
22 Jun 2026 · 53 min

#365 Your 90 Day Blueprint for AI Success with Charlene Li, Author of Winning with AI

Most organizations know AI matters, but few have turned that conviction into a written plan. Ambition and hope are everywhere; a clear roadmap tied to business strategy is rare. For teams on the ground, this gap shows up as scattered initiatives, tools nobody fully uses, and a lot of activity that never adds up to real value. So where do you actually start? How do you move from a long list of use cases to a focused plan you can execute? And who in the organization should own the job of turning AI into business results? Charlene Li is a New York Times bestselling author and strategic advisor…
15 Jun 2026 · 44 min

#364 How to Enable Agentic Commerce with Nell Thomas, VP of Data at Shopify

AI agents are starting to handle parts of the shopping journey that used to require human judgment — discovery, comparison, checkout. But behind every agent recommendation is a massive, invisible layer of data infrastructure. Product catalogs need to be structured, inventory synced in real time, pricing accurate, and quality signals clear. For data engineers and teams building at companies like Shopify, this shift means rethinking how data flows through systems and what "good enough" quality actually means. How do you ensure data is ready for AI? And how is this reshaping what data teams…
8 Jun 2026 · 54 min

#363 Build Your Personal Brand at Work | Dorie Clark, Executive Education Faculty at Columbia Business School

Technical skills are being commoditized faster than ever. As AI takes on more of the work that used to define a junior knowledge worker, the things that once made someone valuable are becoming table stakes. What compounds in this environment is reputation — what colleagues, clients, and decision-makers think about you when your name comes up. That puts new pressure on visibility. People doing great work in silence are increasingly the ones getting passed over for promotions and external opportunities. So how do you build a reputation without becoming an influencer? What does AI-era…
1 Jun 2026 · 48 min

#362 How to Have a Machine Learning Career in 2026 | Marina Wyss, Senior Applied Scientist at Twitch

The role of the machine learning engineer is being rewritten in real time. AI coding assistants are absorbing parts of the day-to-day, planning and evaluation are eating up more of the week, and the lines between machine learning engineer, AI engineer, and data scientist are blurrier than ever. For anyone working in data and AI — or trying to break in — this shift changes what skills are worth investing in, what employers actually screen for, and how interviews are run. What's still worth learning? What does a competitive portfolio look like? And how do you stand out when a thousand…
25 May 2026 · 49 min

#361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at Saks

Every conversation about AI in data eventually arrives at the same question: which roles survive, and which ones get automated away? Generative AI can already draft SQL, build dashboards, and run exploratory analysis — but it still can't sit with a business stakeholder and untangle what "customer" actually means across five teams. For data professionals, that shifts the day-to-day from production work toward translation, modeling, and judgment. So which skills are worth doubling down on? Which roles are becoming central, and which are quietly disappearing? And what should anyone hiring — or…
18 May 2026 · 57 min

#360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue Consultants

Most AI ethics conversations sound the same: be fair, be transparent, be accountable. The values are right, but in practice they don't get teams out of bed in the morning. Executives nod along, employees take the compliance training, and meanwhile real risks like hallucinations, cascading failures, and autonomous agents acting at scale slip through. So what shifts when teams stop chasing an ethical ideal and start naming the specific disasters they want to avoid? Who needs to be in the room to spot them? And what kind of training actually changes how people use AI day to day? Reid Blackman…
12 May 2026 · 44 min

#359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of Minnesota

Valerie Tiberius is the Paul W. Frenzel Chair in Liberal Arts and Professor of Philosophy at the University of Minnesota. She is an expert in ethics, moral psychology, and well-being, and the author of five books including What Do You Want Out of Life? and the forthcoming Artificially Yours: Real Friendship in a World of Chatbots (Princeton University Press, May 2026). She previously served as President of the Central Division of the American Philosophical Association. In the episode, Richie and Valerie explore the purpose of friendship and whether AI can replicate it, the benefits and risks…
4 May 2026 · 58 min

#358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie Mellon

Almost every AI agent demo lands in roughly the same place: it works most of the time, looks remarkable, and then fails in a way no one anticipated. Self-driving cars hit this wall a decade ago, and agents are running into it now. For data and AI teams, the question is no longer whether agents can complete a task — it's whether they can complete it reliably enough to remove the human reviewer. Which categories of work tolerate a 90% success rate? Which absolutely don't? And where should the next layer of guardrails sit? Ruslan Salakhutdinov is a UPMC Professor of Computer Science at Carnegie…
27 Apr 2026 · 58 min

#357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio Labs

The data field has changed shape faster than almost any other. The role that used to be a statistician became a data scientist, became an ML engineer, and is now morphing into AI engineer. Consulting firms are hiring fewer entry-level analysts and more vibe-coders who can ship AI systems to production. For data and AI professionals, this raises immediate questions. Which parts of the work are most exposed to automation, and which are not? Where should you invest your time? And which backgrounds are now producing the strongest hires, whether you are building a team or trying to join one? Ben…
20 Apr 2026 · 54 min

#356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at Apple

Time series data is everywhere — from inventory systems and energy grids to financial planning and product demand. As data volumes grow, the old ways of building individual forecasting models simply don't scale. How do you forecast hundreds of thousands of products without spending months on manual modeling? How do you know when to trust automation and when to step in? And what does it actually take to produce forecasts that business stakeholders will act on? Rami Krispin is Senior Director of Data Science and Engineering at Apple Finance, where he leads teams working at the intersection of…
13 Apr 2026 · 53 min

#355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at Microsoft

Cloud data platforms now offer hundreds of services, plus a growing menu of SQL, NoSQL, and open source options. Unified environments promise a simpler path, but the hard trade-offs—consistency versus scale, single-writer versus sharded, RPO/RTO targets—still matter. In daily work, you may be deciding between SQL Server, Postgres, and a globally distributed JSON store, while also asking AI tools to draft queries and spot issues. Should you still learn SQL if an agent can write it? How do you validate the intent, performance, and security of generated queries? And can monitoring agents…
6 Apr 2026 · 46 min

#354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at Quantexa

Decision intelligence is showing up across data and AI teams as companies move beyond dashboards to decisions made with context. Graphs, entity resolution, and better data products are becoming core tools as messy, siloed data meets stricter risk and compliance needs. In day-to-day work, this means linking “James,” “Jim,” and “Jamie” across systems, enriching records with third‑party sources, and pushing models where the data already lives in your lakehouse. How do you trust your customer counts? Which links in a graph matter, and which are noise? Can graph-based context reduce LLM…
30 Mar 2026 · 50 min

#353 The Data Team's Agentic Future with Ketan Karkhanis, CEO at ThoughtSpot

Data and AI platforms are racing toward agentic and even autonomous analytics. But the bottleneck is rarely the model—it’s data readiness: governed metrics, clear metadata, and a semantic layer machines can read. For data engineers and analysts, this shifts work from hand-built SQL and dashboard tweaks to designing meaning and trust. If an agent can draft column descriptions, propose a model for a new business question, and build the first dashboard layout, where do you add the most value? What do you measure to prove ROI in 30 days? How do you prevent “shiny demos” from driving strategy too…
23 Mar 2026 · 56 min

#352 AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop, EVP Digital Strategy & Alliances at WNS

AI agents are spreading across the data and AI industry, promising to automate everything from research to outreach. At the same time, teams are learning that these tools can hallucinate, leak data, or act in surprising ways. In day-to-day work, the challenge is deciding which tasks to hand off, what data to share, and how to keep the output trustworthy. Do your agents actually add value, or just add noise? Are they running in a secured, ring-fenced environment? How do you balance playful experimentation with critical checking when an agent confidently gets a key fact wrong? Danielle leads…
16 Mar 2026 · 1 hr 4 min

#351 Will World Models Bring us AGI? with Eric Xing, President & Professor at MBZUAI

World models are emerging as the next step after large language models, pushing AI from book knowledge toward systems that can simulate the physical and social world. Instead of just generating text or short videos, the goal is steerable simulation with long-horizon consistency and planning. For practitioners, this raises practical choices: what data and representations do you need, and when do you mix symbolic reasoning with generative models? How do you test whether a model can follow actions over minutes, not seconds? And where do you start—robotics, driving safety, or synthetic data…

About DataFramed

Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone. Join host Richie Cotton as he delves into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.

DataFramed is a English tech & science podcast from DataCamp. Melo plays each episode straight from the publisher's own feed — no ads added, no account needed — and remembers where you stopped, in this browser only.

Publisher: DataCamp
Language: English
Category: Tech & Science
New episodes: Weekly
Typical length: 49 min
Latest episode: 7 Oct 2026
Feed: RSS — paste into any podcast app
Apple Podcasts: podcasts.apple.com/podcast/id1336150688
Website: datacamp.com/podcast

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