DataFramed

DataFramed

Kirjailija
- DataCamp

0 Arviota: 0
Jaksot: 299
Keskimääräinen kesto: 52M
Kieli: inglise
Formaatti
Kategoria: Majandus ja ettevõtlus

Episodes

#363 Build Your Personal Brand at Work | Dorie Clark, Executive Education Faculty at Columbia Business SchoolTechnical 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 credibility actually look like? And how do you start small? Dorie Clark teaches Executive Education at Columbia Business School and is the Wall Street Journal and USA Today bestselling author of The Long Game, Entrepreneurial You, Reinventing You, and Stand Out. She has been named four times as one of the Top 50 business thinkers in the world by Thinkers50, recognized as the #1 Communication Coach in the world by the Marshall Goldsmith Leading Global Coaches Awards, and is a frequent contributor to the Harvard Business Review. In the episode, Richie and Dorie explore why AI fluency is the new Excel skill, tinkering with AI's jagged frontier, the security risks of agentic AI, what personal branding really means in an AI-disrupted job market, the recognized expert formula, the ladder strategy for credibility, networking with "no asks for a year," running better meetings, and much more. Links Mentioned in the Show: • The Jagged Frontier (HBS Working Paper) • Agentic Misalignment: How LLMs could be insider threats (Anthropic) • AI-powered coding tool wiped out a software company's database (Fortune) • Reinventing You by Dorie Clark • The Long Game by Dorie Clark • Superteams by Ron Friedman • Connect with Dorie on LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: #341 Our Data Trends & Predictions for 2026 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 • .
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#362 How to Have a Machine Learning Career in 2026 | Marina Wyss, Senior Applied Scientist at TwitchThe 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 applicants are using bots to apply? Marina Wyss is a Senior Applied Scientist at Twitch (an Amazon company), where she builds production AI and machine learning systems across content understanding, recommendations, and forecasting. She came into the field from a non-traditional background — a political science undergrad and a Master's in social data science in Berlin — and has held machine learning roles at Coursera and a Berlin-based statistical consultancy along the way. Outside her day job, Marina runs a popular AI/ML YouTube channel and weekly newsletter, and coaches people transitioning into machine learning from non-traditional careers. In this episode, Richie and Marina explore how AI is reshaping the machine learning engineer role, the shifting balance between coding and planning, why evaluation matters more than ever, the differences between ML engineer, AI engineer, and data scientist roles, how to break into the field from a non-technical background, what makes a strong portfolio project, the hiring process at big tech, how to prepare for technical interviews, networking strategies that actually work, what success looks like in your first few months on the job, and much more. Links Mentioned in the Show • Chip Huyen — AI Engineering (book) • Andrew Codesmith on YouTube • Phillip Choi on YouTube • A Life Engineered on YouTube • Keras • LeetCode • Connect with Marina: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: How to Have a Career in Data Science in 2025 with Dawn Choo New to DataCamp? • Learn on the go using the DataCamp mobile app - https://www.datacamp.com/mobile • Empower your business with world-class data and AI skills with DataCamp for business - https://www.datacamp.com/business
387|47M
#361 If You Want AI to Work, Fix This Boring Thing First with Veronika Durgin, VP of Data at SaksEvery 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 being hired — be paying attention to right now? Veronika Durgin is the VP of Data at Saks Global, where she leads data strategy across the luxury retail group. A full-stack data executive with more than two decades of experience spanning database administration, data engineering, platform architecture, data modeling, and analytics, Veronika is a Snowflake Data Superhero and a member of CDO Magazine's Global Editorial Board. She writes about data modeling, data culture, and data leadership on her Substack and Medium. In the episode, Richie and Veronika explore the future of data careers under AI, why analytics engineering becomes the catch-all role, the skills and hiring shifts data leaders are making, centralized data with decentralized analytics, keeping enterprise data teams agile, conceptual data modeling as the unglamorous prerequisite to AI, semantic layers, agentic commerce, and much more. Links Mentioned in the Show: • Connect with Veronika: LinkedIn • Veronika's Substack: Think. Solve. Repeat.dbt • — referenced as the origin of "analytics engineering" Open Data Science Conference (ODSC) • — Veronika's recent talk on data and company politics Amazon "two-way door" decisions • — Bezos shareholder letter Jessica Talisman • — Veronika's recommendation for knowledge graphs and ontologies Juan Sequeda • — referenced on semantic layers and knowledge graphs Catalog & Cocktails podcast • (hosted by Juan Sequeda) • AI-Native Course: Intro to AI for Work • Related Episode: Creating an AI-First Data Team with Bilal Zia, Head of Data Science & Analytics at Duolingo 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
386|48M
#360 What's Your Biggest AI Ethical Nightmare? | Reid Blackman, CEO at Virtue ConsultantsMost 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 is the founder and CEO of Virtue, an AI ethical risk consultancy, and the author of The Ethical Nightmare Challenge: How to Avoid the Worst of AI (2026) and Ethical Machines (HBR Press, 2022). A former philosophy professor at Colgate with a PhD from the University of Texas at Austin, he has designed responsible AI programs for organizations including Amazon, Etsy, Kraft Heinz, Merck, US Bank, and Nationwide, and has advised the FBI, NASA, the World Economic Forum, and the Canadian government on federal AI regulations. He also hosts the Ethical Machines podcast. In the episode, Richie and Reid explore why responsible AI fails to motivate organizations, the biggest AI ethical nightmares facing companies today, the unique risks of agentic AI including cascading failures and emergent risks, the Ethical Nightmare Challenge framework, cross-functional ENC teams, training employees in plain language, scaling AI governance, measuring success by what you avoid, and much more. Links Mentioned in the Show: • The Ethical Nightmare Challenge by Reid Blackman • Ethical Machines by Reid Blackman • Ethical Machines podcast • Claude Code • Connect with Reid: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: #350 How to Make Hard Choices in AI with Atay Kozlovski 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.
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#359 My Best Friend is AI with Valerie Tiberius, Professor of Philosophy at University of MinnesotaValerie 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 of chatbot companions for loneliness, how sycophantic AI responses distort advice and self-perception, the dangers of companion chatbots for children's social development, designing ethical AI companions that promote human flourishing, the zone of proximal development as a framework for better AI tools, and much more. Links Mentioned in the Show: Artificial Intimacy by Sherry Turkle • Being You: A New Science of Consciousness • by Anil Seth Liberation Day: Stories • by George Saunders Hard Fork podcast (NYT • ) Connect with ValerieAI-Native Course: Intro to AI for Work • Related Episode: #342 — "The Secrets to High AI Adoption" with Stefano Puntoni, Professor at Wharton 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
384|43M
#358 How AI Agents Will Work While You Sleep | Ruslan Salakhutdinov, Professor at Carnegie MellonAlmost 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 Mellon University and one of Geoffrey Hinton's former PhD students. He has previously served as Director of AI Research at Apple and VP of Research in Generative AI at Meta. His research focuses on deep learning, reasoning, and AI agents. In the episode, Richie and Russ explore the most exciting use cases of AI agents today, long horizon tasks, the credit assignment problem, multi-agent systems, designing reliable human-in-the-loop workflows, agent safety and guardrails, embodied and physical AI, lessons from self-driving cars, the difference between academia and industry, and much more. Links Mentioned in the Show: • Claude Code (Anthropic) • Yutori • Waymo • Apple Project Titan • DeepSeek-V3 Technical Report • Kimi K2 Technical Report • Connect with Ruslan: LinkedIn • AI-Native Course: Intro to AI for Work • Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It) with Danielle Crop 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
383|58M
#357 Data-Driven Workforce Analytics with Ben Zweig, CEO at Revelio LabsThe 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 Zweig is the CEO and Co-Founder of Revelio Labs, where he leads the development of a universal HR database built on over a billion public employment profiles and more than 5 billion job postings. He holds a PhD in Economics from the CUNY Graduate Center and teaches Data Science and The Future of Work at NYU Stern. Before founding Revelio Labs, he managed Workforce Analytics projects in the IBM Chief Analytics Office and worked as a data scientist at an emerging-markets hedge fund. He is the author of Job Architecture: Building a Workforce Intelligence Taxonomy. In the episode, Richie and Ben explore why hiring is a broken two-sided market, why jobs are bundles of tasks not skills, building universal taxonomies from billions of job postings, which data careers resist AI, advice for hiring data talent, when traditional NLP beats LLMs, and much more. Links Mentioned in the Show: Ben's book — Job Architecture: Building a Workforce Intelligence TaxonomyRevelio LabsO*NET — the US government occupational taxonomy Ben critiquesBaruch Lev — The End of AccountingHaskel & Westlake — Capitalism Without CapitalJustified Posteriors podcast (Andrey Fradkin & Seth Benzell) • Connect with Ben: LinkedInAI-Native Course: Intro to AI for Work • Related Episode: Our Data Trends & Predictions for 2026 with Jonathan Cornelissen & Martijn Theuwissen 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
382|58M
#356 The Forecast for Time Series Forecasts with Rami Krispin, Senior Manager of Data Science at AppleTime 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 statistical modeling, machine learning, and production forecasting. He is the author of Hands-On Time Series Analysis with R, an open-source contributor, Docker Captain, and instructor. He holds an MA in Applied Economics and an MS in Actuarial Mathematics from the University of Michigan, where he began his journey learning time series on DataCamp — before going on to build his own course there. In the episode, Richie and Rami explore time series foundation models and the case for scaling, traditional versus modern forecasting approaches, feature engineering in the business world, backtesting and model selection, risk management in automated forecasting, communicating forecast uncertainty to stakeholders, the evolving role of data scientists as architects, and much more. Links Mentioned in the Show: Forecasting: Principles and Practice • (Rob Hyndman) NixtlaskforecastProphet • Connect with RamiAI-Native Course: Intro to AI for Work • Related Episode: Developing Better Predictive Models with Graph Transformers 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
381|53M
#355 AI's Impact on Databases with Shireesh Thota, CVP of Databases at MicrosoftCloud 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 actually reduce on-call pain without taking away needed control? Shireesh is the CVP of Databases at Microsoft. He leads product management, engineering, and cloud operations for Azure Databases as well as App Development for Microsoft Fabric. The products in his team’s portfolio include Azure SQL Database (on-prem, Hybrid and Cloud), Azure Cosmos DB, Azure PostgreSQL, and Azure MySQL.\\n\\n Previously, as the Senior Vice President at SingleStore, Shireesh was responsible for end-to-end engineering and product vision of the company. Before moving to SingleStore, Shireesh was a founding member of Cosmos DB, where he architected, designed, and directly contributed to multiple key pieces of the services.\\n\\n Shireesh has 20+ years of experience on large scale, big data, scale-out, relational and schema agnostic distributed systems across SQL, Azure Cosmos DB and PostgreSQL/Citus. In the episode, Richie and Shireesh explore how AI agents are reshaping data stacks, why unified platforms like Fabric matter, how semantic models and ontologies reduce confusion in metrics, SQL and NoSQL choices on Azure, Postgres to Cosmos DB with guidance for builders, and much more. Links Mentioned in the Show: Microsoft FabricAzure Cosmos DBWhat is Azure SQL Database?Connect with ShireeshAI-Native Course: Intro to AI for WorkRelated Episode: Six Skills Data Professionals Need To Succeed with Abhijit Bhaduri, Brand Evangelist & Former General Manager of Global L&D at MicrosoftExplore AI-Native Learning on DataCamp 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
380|52M
#354 Beyond BI: Decision Intelligence with Graphs with Jamie Hutton, CTO at QuantexaDecision 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 hallucinations enough for regulated decisions with humans still in-loop. Jamie Hutton is the Co-founder and Chief Technology Officer of Quantexa, where he leads the company’s global research and development organization in advancing its market-leading Decision Intelligence Platform. With over two decades of experience pioneering data-driven technologies, Jamie has been at the forefront of innovations that connect and unify data at scale to solve complex real-world challenges. He is the creator of dynamic Entity Resolution, a pioneering capability that has redefined how the world’s leading organizations transform raw data into trusted, decision-ready intelligence. This innovation enables enterprises to prepare their data for AI, uncover new revenue streams, and expose hidden connections in even the most sophisticated criminal networks. By providing the foundation for accurate, explainable, and actionable insights, Jamie’s work has empowered governments, financial institutions, and global enterprises to make faster, smarter, and more confident decisions. Prior to co-founding Quantexa, Jamie held senior technology and analytics leadership roles at SAS and Detica, where he delivered mission-critical solutions for organizations operating in some of the most complex and high-stakes environments in the world. Jamie holds a First-Class master’s degree in computer engineering and is recognized as a leading authority in contextual analytics, data integration, and applied AI for mission-critical decision-making. In the episode, Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, credit risk, and growth, how graph analytics separates meaningful links from noise, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more. Links Mentioned in the Show: QuantexaDun & Bradstreet Data EnrichmentConnect with JamieAI-Native Course: Intro to AI for WorkRelated Episode: How Optimization Powers Decision Intelligence with Duke Perrucci & Ed Klotz, CEO and Senior Mathematical Optimization Specialist at Gurobi OptimizationExplore AI-Native Learning on DataCamp 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
379|46M

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