Whether they’re part of a workflow or powering a user experience, AI agents often need to work on your data at interactive speeds. Because of this, they demand more from data systems than traditional analytics workloads. Low latency is no longer a luxury, and handling concurrency is essential if you want an agent to scale with a growing platform. But with great access comes great risk: safeguards and strict permission models are critical. This talk will explore the principles and patterns for building responsive, reliable data infrastructure that agents can trust and users can depend on.

Send us a text What happens when AI hype collides with enterprise reality? Tim Leers, Global Generative & Agentic AI Lead at Dataroots, pulls back the curtain on what's actually working—and what's not—in enterprise AI deployment today.

We begin by examining why companies like Klarna publicly announced replacing customer service teams with AI, only to quietly backtrack months later when quality suffered. This pattern of inflated expectations followed by reality checks has become common, creating what Tim calls "AI theater" – impressive demos with minimal business impact.

The conversation tackles the often misunderstood concept of "agentic AI." Rather than viewing it as a specific technology, Tim frames agency as fundamentally about delegated authority – the ability to trust AI systems with meaningful responsibilities. However, this delegation requires contextual intelligence—providing the right data at the right time—which most organizations struggle to implement effectively.

"Models are commodities, data is your moat," Tim explains, arguing that proprietary business context will remain the key differentiator even as AI models continue advancing. This perspective challenges the conventional wisdom that focuses primarily on model capabilities rather than data infrastructure.

Perhaps most valuably, Tim outlines three pillars for successful enterprise AI: contextual intelligence, continuous improvement (designing systems that evolve with changing business contexts), and human-AI collaboration. This framework shifts focus from technology deployment to sustainable business value creation.

The discussion concludes with eight practical lessons for organizations implementing generative AI, from avoiding the temptation to build proprietary models to recognizing that teaching employees to prompt effectively isn't sufficient for enterprise-wide adoption. Each lesson reinforces a central theme: successful AI implementation requires designing for change rather than building rigid systems that quickly become obsolete.

Whether you're a technical leader evaluating vendor claims or a business executive trying to separate AI reality from fantasy, this episode provides the practical guidance needed to move beyond the hype cycle toward meaningful implementation.

Beat instant rejections. Use an AI resume audit to pass ATS filters and turn silence into interviews—clear steps, a one-week plan, and a free checker. AI job search without the guesswork. In this episode I use a tiny AI resume & portfolio audit to beat ATS filters—what to highlight, what’s missing, and how to rewrite one project so a hiring manager actually cares. It’s personal, practical, and ends with a one-week plan you can apply today. You’ll learn • How modern ATS screeners work—and why they’re fast (and unforgiving) • The simple AI workflow behind my ATS simulator (no hype, just outcomes) • Three lessons from failing my own test—and what actually moved the score • How to translate your story so it passes the bots and reaches humans • A one-week action plan to raise your odds on your next application Key takeaways • ATS = gatekeeper. If you don’t pass it, humans may never see you. • Match keywords exactly from the JD—“close enough” doesn’t count. • ATS-friendly formatting beats fancy templates that break parsing. • Quantify outcomes so machines and recruiters see impact. • Test before you apply with an ATS checker/simulator. Try this today (no code) Paste into your AI tool of choice: “Here’s my resume + 3 project summaries and the job description I’m applying to. 1) What should I highlight to match the JD? 2) What am I missing? 3) Rewrite one project to emphasize measurable business outcomes in 2–3 bullets.” One-week plan Day 1: Baseline ATS check; log gaps. Day 2: Map exact JD keywords to your resume/projects. Day 3: Rewrite top project in outcome language (numbers first). Day 4: Fix formatting (simple headings, standard section names). Day 5: Add two quantified wins; remove tool-only bullets. Day 6: Align portfolio links to the role (pin your best two). Day 7: Re-test; apply to three roles; track results. Resources Full story + DIY steps: https://medium.com/data-science-collective/when-an-ai-tool-i-built-evaluated-my-resume-i-learned-what-100-rejections-never-taught-me-8e8eea1f3d8fRecommended: use any reputable ATS checker to preview parsing before you apply.Affiliate Disclosure This episode may contain affiliate links. If you purchase via these links, I may earn a small commission at no extra cost to you. Thanks for supporting the show. Affiliate partners (links below): RSS: your podcast, get free transcripts, and earn ad revenue with as few as 10 monthly downloads. Sign up here.Sider AI. AI-powered research and productivity assistant for breaking down job descriptions into keywords. Try Sider here.Riverside FM: Record your podcast in studio-quality audio and 4K video from anywhere. Get started with Riverside here.Do this next Run your resume through an ATS checker this week. Find the gaps. Fill them. If this helped, share with a friend who’s job hunting and follow/subscribe for more real-world AI workflows.

Is AI the silver bullet for modernizing our aging software systems, or is it a fast track to creating the next generation of unmaintainable "slopware"?In this episode, I sit down with Marianne Bellotti, author of the amazing book "Kill It With Fire," to discuss the complex reality of legacy system modernization in the age of AI. We explore why understanding the cultural and human history of a codebase is critical, and how the current AI hype cycle isn't a silver bullet for legacy IT modernization efforts.Marianne breaks down a recent disastrous "vibe coding" experiment, the risk of replacing simple human errors with catastrophic automated ones, and the massive disconnect between the promises of AI agents and the daily reality of a practitioner just trying to get a service account from IT.Join us for a pragmatic and no-BS conversation about the real challenges in software, the practical ways to leverage LLMs as an expert partner, and why good old-fashioned systems thinking is more important than ever.Find Marianne Bellotti:Socials: @BellmarWebsite: https://belladotte.tech/Book, "Kill It With Fire": https://nostarch.com/kill-it-fire

Está no ar, o Data Hackers News !! Os assuntos mais quentes da semana, com as principais notícias da área de Dados, IA e Tecnologia, que você também encontra na nossa Newsletter semanal, agora no Podcast do Data Hackers !! Aperte o play e ouça agora, o Data Hackers News dessa semana ! Para saber tudo sobre o que está acontecendo na área de dados, se inscreva na Newsletter semanal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.datahackers.news/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Acesse os links: ⁠Inscrições do Data Hackers Challenge 2025⁠ ⁠Live Zoho: Decisões Baseadas em Dados: Aplicando Machine Learning com o Zoho Analytics Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠Matérias/assuntos comentados: Live finalistas do Data Hackers Challenge 2025; Evento Mettup Itaú Matéria lançamento ChatGpt5; Matéria Elon Musk libera Grok grátis nos EUA. Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

The relationship between AI and data professionals is evolving rapidly, creating both opportunities and challenges. As companies embrace AI-first strategies and experiment with AI agents, the skills needed to thrive in data roles are fundamentally changing. Is coding knowledge still essential when AI can generate code for you? How important is domain expertise when automated tools can handle technical tasks? With data engineering and analytics engineering gaining prominence, the focus is shifting toward ensuring data quality and building reliable pipelines. But where does the human fit in this increasingly automated landscape, and how can you position yourself to thrive amid these transformations? Megan Bowers is Senior Content Manager, Digital Customer Success at Alteryx, where she develops resources for the Maveryx Community. She writes technical blogs and hosts the Alter Everything podcast, spotlighting best practices from data professionals across the industry. Before joining Alteryx, Megan worked as a data analyst at Stanley Black & Decker, where she led ETL and dashboarding projects and trained teams on Alteryx and Power BI. Her transition into data began after earning a degree in Industrial Engineering and completing a data science bootcamp. Today, she focuses on creating accessible, high-impact content that helps data practitioners grow. Her favorite topics include switching career paths after college, building a professional brand on LinkedIn, writing technical blogs people actually want to read, and best practices in Alteryx, data visualization, and data storytelling. Presented by Alteryx, Alter Everything serves as a podcast dedicated to the culture of data science and analytics, showcasing insights from industry specialists. Covering a range of subjects from the use of machine learning to various analytics career trajectories, and all that lies between, Alter Everything stands as a celebration of the critical role of data literacy in a data-driven world. In the episode, Richie and Megan explore the impact of AI on job functions, the rise of AI agents in business, and the importance of domain knowledge and process analytics in data roles. They also discuss strategies for staying updated in the fast-paced world of AI and data science, and much more. Links Mentioned in the Show: Alter EverythingConnect with MeganSkill Track: Alteryx FundamentalsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Send us a text Episode Description (Show Notes): Step into the world of IBM Power Systems with insider insights from Tom McPherson, former GM of IBM Power. In this conversation, Tom shares leadership lessons, debunks common misconceptions, and dives deep into the innovations shaping the future of Power infrastructure. From AI integration to hybrid cloud strategies, competitive positioning to compelling client use cases — it’s a powerhouse discussion you won’t want to miss. Timestamps:  00:49 Meet Tom McPherson 03:00 Leadership Advice 04:58 Hobbies 07:53 IBM Power 10:24 Power 11 13:53 Common Misconception 14:39 Favorite Power Features 21:51 Promise to Profits of AI 25:28 Hybrid Cloud 27:34 Power Competitors 28:36 Compelling Use Cases 29:51 The Future of Power 31:20 Rapid Fire 33:51 Business Partners in Power 35:16 LeadershipGuest Links: 🔗 Tom McPherson on LinkedIn 🌐 IBM Power Systems Social: #IBMPower #Leadership #HybridCloud #AI #EnterpriseTech #TechInnovation #MakingDataSimple #PowerSystems #BusinessStrategy #DigitalTransformation #CloudComputing #AIinBusiness Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

In this episode, we explore a high-tech twist on developmental toxicology. Researchers have combined microfluidic engineering with machine learning to automate the analysis of thousands of C. elegans for chemical toxicity testing — no anaesthetics or low-res imaging required.

Using the vivoChip device and a custom ML model called vivoBodySeg, the team:

Captures 3D images of ~1000 worms from 24 populations at once Achieves near-human segmentation accuracy (Dice score: 97.8%) Measures subtle toxicity effects like changes in body size and gut autofluorescence Identifies EC10 and LOAEL values with high precision Uses few-shot learning to adapt the model to new worm shapes and sizes

This platform slashes analysis time by 140× and sets a new benchmark for high-throughput New Approach Methodologies (NAMs) in toxicology.

📖 Based on the research article: “Machine learning-based analysis of microfluidic device immobilised C. elegans for automated developmental toxicity testing” Andrew DuPlissis, Abhishri Medewar, Evan Hegarty, et al. Published in Scientific Reports (2025) 🔗 https://doi.org/10.1038/s41598-024-84842-x

🎧 Subscribe to the WOrM Podcast for more stories where whole-organism biology meets cutting-edge tech!

This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]

Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining. Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building robust graph learning systems in a world of dynamic and evolving graphs. Understand the importance of graph learning for boosting enterprise-grade applications Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines Use traditional and advanced graph learning techniques to tackle graph use cases Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications Design and implement a graph learning algorithm using publicly available and syntactic data Apply privacy-preserving techniques to the graph learning process

Leveraging Project files and TMDL for agent reasoning

What AI and generative AI can do for your role

How to integrate AI-powered tasks into your daily routine

Interactive icebreaker and why AI matters for modern work

Description: Join Mukundan Sankar as he explores the challenges of delivering an effective elevator pitch and how AI can assist in crafting one. Mukundan shares personal anecdotes and demonstrates AI-generated pitches tailored for different career stages. Key Takeaways: The importance of a well-crafted elevator pitch How AI can personalize pitches for different roles Real-life examples of AI-generated pitches Resources: 1]Elevator Pitch AI Code Mukundan's Blog Post: https://substack.com/home/post/p-170400977 2] Thinking about starting a podcast but worried it’ll take forever to grow? Here’s the thing — you don’t need a huge audience to get started or to earn money. I run my show on RSS.com, and it’s the simplest way to get your podcast live on Spotify, Apple, Amazon, YouTube, iHeartRadio, Deezer, and more — all in one step. Their analytics tell me exactly where my listeners are tuning in from, so I know what’s working. And here’s the best part — with their paid plan, you can start earning revenue through ads with as little as 10 downloads a month. That’s right — you don’t need to wait for thousands of listeners to start monetizing. Start your podcast for free today at RSS.com. (Affiliate link — I may earn a commission at no extra cost to you.) 3] 💡 Sider.ai– Your AI Copilot for Productivity: Sider.ai is the all-in-one AI assistant that works inside your browser, letting you research, write, summarize, and brainstorm without switching tabs. Whether you’re prepping for an interview, drafting your next pitch, or refining your business plan, Sider.ai can supercharge your productivity. It’s like having GPT-4 on standby, ready to help you think faster and write better. Try Sider.ai today and see how much more you can accomplish in less time. (Affiliate link — I may earn a commission if you sign up.)

Reco AI presentation on switching to ClickHouse as a PostgreSQL alternative.

Formation immersive animée en direct par un formateur expert. En 3 heures, passez de novice à explorateur du code en créant des logiciels concrets (calculatrice, outil de tirage au sort, convertisseur d’image, et bien plus). Vous découvrirez les bases du développement web, les API et même l’intelligence artificielle et le deep learning, le tout avec des outils accessibles et ludiques. Une approche humaine et interactive pour gagner en autonomie.

Today, we’re joined by  Chris Silvestri, Founder at Conversion Alchemy, an agency that combines copywriting, UX design, and psychology to help SaaS and eCommerce companies convert more visitors into customers. We talk about:  How failure to crystallize strategy results in messaging shortcomings & low conversionsTactics to get started with & accelerate messaging content, including use of AIImpacts of improving messaging to differentiate your SaaS offeringGrowth stages at which it’s most impactful to fine-tune messagingUse of AI models to act as prospects in order to gain insights, including use of real research to construct partially synthetic personas

What does it take to land a data analyst job at Tesla, and what challenges await you once you're there? Join me as I interview Lily BL, a former Tesla data analyst, who reveals her exhilarating journey in the world of data at one of the world's most innovative companies. 💌 Join 10k+ aspiring data analysts & get my tips in your inbox weekly 👉 https://www.datacareerjumpstart.com/newsletter 🆘 Feeling stuck in your data journey? Come to my next free "How to Land Your First Data Job" training 👉 https://www.datacareerjumpstart.com/training 👩‍💻 Want to land a data job in less than 90 days? 👉 https://www.datacareerjumpstart.com/daa 👔 Ace The Interview with Confidence 👉 https://www.datacareerjumpstart.com/interviewsimulator ⌚ TIMESTAMPS 00:00 - Introduction 00:31 - Working on Data Projects at Tesla 01:45 - Was it challenging working at Tesla? 08:34 - Hiring Process and Employee Evaluation 11:56 - Tools and Technologies Used 13:38 - Lily Landing the Job at Tesla 15:42 - Advice for Aspiring Data Professionals 19:36 - How the Data Analytics Accelerator helped Lily 25:11 - Data Analyst Titles Matrix 29:50 - Linking Business Needs to Data Solutions 🔗 CONNECT WITH LILY BL 🤝 LinkedIn: https://www.linkedin.com/in/lilybl/

🔗 CONNECT WITH AVERY 🎥 YouTube Channel: https://www.youtube.com/@averysmith 🤝 LinkedIn: https://www.linkedin.com/in/averyjsmith/ 📸 Instagram: https://instagram.com/datacareerjumpstart 🎵 TikTok: https://www.tiktok.com/@verydata 💻 Website: https://www.datacareerjumpstart.com/ Mentioned in this episode: Join the last cohort of 2025! The LAST cohort of The Data Analytics Accelerator for 2025 kicks off on Monday, December 8th and enrollment is officially open!

To celebrate the end of the year, we’re running a special End-of-Year Sale, where you’ll get: ✅ A discount on your enrollment 🎁 6 bonus gifts, including job listings, interview prep, AI tools + more

If your goal is to land a data job in 2026, this is your chance to get ahead of the competition and start strong.

👉 Join the December Cohort & Claim Your Bonuses: https://DataCareerJumpstart.com/daa https://www.datacareerjumpstart.com/daa

Summary In this episode of the Data Engineering Podcast Lucas Thelosen and Drew Gilson from Gravity talk about their development of Orion, an autonomous data analyst that bridges the gap between data availability and business decision-making. Lucas and Drew share their backgrounds in data analytics and how their experiences have shaped their approach to leveraging AI for data analysis, emphasizing the potential of AI to democratize data insights and make sophisticated analysis accessible to companies of all sizes. They discuss the technical aspects of Orion, a multi-agent system designed to automate data analysis and provide actionable insights, highlighting the importance of integrating AI into existing workflows with accuracy and trustworthiness in mind. The conversation also explores how AI can free data analysts from routine tasks, enabling them to focus on strategic decision-making and stakeholder management, as they discuss the future of AI in data analytics and its transformative impact on businesses.

Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data managementData migrations are brutal. They drag on for months—sometimes years—burning through resources and crushing team morale. Datafold's AI-powered Migration Agent changes all that. Their unique combination of AI code translation and automated data validation has helped companies complete migrations up to 10 times faster than manual approaches. And they're so confident in their solution, they'll actually guarantee your timeline in writing. Ready to turn your year-long migration into weeks? Visit dataengineeringpodcast.com/datafold today for the details.Your host is Tobias Macey and today I'm interviewing Lucas Thelosen and Drew Gilson about the engineering and impact of building an autonomous data analystInterview IntroductionHow did you get involved in the area of data management?Can you describe what Orion is and the story behind it?How do you envision the role of an agentic analyst in an organizational context?There have been several attempts at building LLM-powered data analysis, many of which are essentially a text-to-SQL interface. How have the capabilities and architectural patterns grown in the past ~2 years to enable a more capable system?One of the key success factors for a data analyst is their ability to translate business questions into technical representations. How can an autonomous AI-powered system understand the complex nuance of the business to build effective analyses?Many agentic approaches to analytics require a substantial investment in data architecture, documentation, and semantic models to be effective. What are the gradations of effectiveness for autonomous analytics for companies who are at different points on their journey to technical maturity?Beyond raw capability, there is also a significant need to invest in user experience design for an agentic analyst to be useful. What are the key interaction patterns that you have found to be helpful as you have developed your system?How does the introduction of a system like Orion shift the workload for data teams?Can you describe the overall system design and technical architecture of Orion?How has that changed as you gained further experience and understanding of the problem space?What are the most interesting, innovative, or unexpected ways that you have seen Orion used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Orion?When is Orion/agentic analytics the wrong choice?What do you have planned for the future of Orion?Contact Info LucasLinkedInDrewLinkedInParting Question From your perspective, what is the biggest gap in the tooling or technology for data management today?Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.Links OrionLookerGravityVBA == Visual Basic for ApplicationsText-To-SQLOne-shotLookMLData GrainLLM As A JudgeGoogle Large Time Series ModelThe intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

Data science continues to evolve in the age of AI, but is it still the 'sexiest job of the 21st century'? While generative AI has transformed the landscape, it hasn't replaced data scientists—instead, it's created more demand for their skills. Data professionals now incorporate AI into their workflows to boost efficiency, analyze data faster, and communicate insights more effectively. But with these technological advances come questions: How should you adapt your skills to stay relevant? What's the right balance between traditional data science techniques and new AI capabilities? And as roles like analytics engineer and machine learning engineer emerge, how do you position yourself for success in this rapidly changing field? Dawn Choo is the Co-Founder of Interview Master, a platform designed to streamline technical interview preparation. With a foundation in data science, financial analysis, and product strategy, she brings a cross-disciplinary lens to building data-driven tools that improve hiring outcomes. Her career spans roles at leading tech firms, including ClassDojo, Patreon, and Instagram, where she delivered insights to support product development and user engagement. Earlier, Dawn held analytical and engineering positions at Amazon and Bank of America, focusing on business intelligence, financial modeling, and risk analysis. She began her career at Facebook as a marketing analyst and continues to be a visible figure in the data science community—offering practical guidance to job seekers navigating technical interviews and career transitions. In the episode, Richie and Dawn explore the evolving role of data scientists in the age of AI, the impact of generative AI on workflows, the importance of foundational skills, and the nuances of the hiring process in data science. They also discuss the integration of AI in products and the future of personalized AI models, and much more. Links Mentioned in the Show: Interview MasterConnect with DawnDawn’s Newsletter: Ask Data DawnGet Certified: AI Engineer for Data Scientists Associate CertificationRelated Episode: How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib AcademyRewatch RADAR AI  New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business