Supported by Our Partners •⁠ Statsig ⁠ — ⁠ The unified platform for flags, analytics, experiments, and more. •⁠ Sinch⁠ — Connect with customers at every step of their journey. •⁠ Cortex⁠ — Your Portal to Engineering Excellence. — What does it take to land a job as an AI Engineer—and thrive in the role? In this episode of Pragmatic Engineer, I’m joined by Janvi Kalra, currently an AI Engineer at OpenAI. Janvi shares how she broke into tech with internships at top companies, landed a full-time software engineering role at Coda, and later taught herself the skills to move into AI Engineering: by things like building projects in her free time, joining hackathons, and ultimately proving herself and earning a spot on Coda’s first AI Engineering team. In our conversation, we dive into the world of AI Engineering and discuss three types of AI companies, how to assess them based on profitability and growth, and practical advice for landing your dream job in the field. We also discuss the following:  • How Janvi landed internships at Google and Microsoft, and her tips for interview prepping • A framework for evaluating AI startups • An overview of what an AI Engineer does • A mini curriculum for self-learning AI: practical tools that worked for Janvi • The Coda project that impressed CEO Shishir Mehrotra and sparked Coda Brain • Janvi’s role at OpenAI and how the safety team shapes responsible AI • How OpenAI blends startup speed with big tech scale • Why AI Engineers must be ready to scrap their work and start over • Why today’s engineers need to be product-minded, design-aware, full-stack, and focused on driving business outcomes • And much more! — Timestamps (00:00) Intro (02:31) How Janvi got her internships at Google and Microsoft (03:35) How Janvi prepared for her coding interviews  (07:11) Janvi’s experience interning at Google (08:59) What Janvi worked on at Microsoft  (11:35) Why Janvi chose to work for a startup after college (15:00) How Janvi picked Coda  (16:58) Janvi’s criteria for picking a startup now  (18:20) How Janvi evaluates ‘customer obsession’  (19:12) Fast—an example of the downside of not doing due diligence (21:38) How Janvi made the jump to Coda’s AI team (25:48) What an AI Engineer does  (27:30) How Janvi developed her AI Engineering skills through hackathons (30:34) Janvi’s favorite AI project at Coda: Workspace Q&A  (37:40) Learnings from interviewing at 46 companies (40:44) Why Janvi decided to get experience working for a model company  (43:17) Questions Janvi asks to determine growth and profitability (45:28) How Janvi got an offer at OpenAI, and an overview of the interview process (49:08) What Janvi does at OpenAI  (51:01) What makes OpenAI unique  (52:30) The shipping process at OpenAI (55:41) Surprising learnings from AI Engineering  (57:50) How AI might impact new graduates  (1:02:19) The impact of AI tools on coding—what is changing, and what remains the same (1:07:51) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: •⁠ AI Engineering in the real world •⁠ The AI Engineering stack •⁠ Building, launching, and scaling ChatGPT Images — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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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/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Conheça nossos comentaristas do Data Hackers News: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Monique Femme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Paulo Vasconcellos Demais canais do Data Hackers: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Site⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tik Tok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠You Tube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Gaëlle Seret is a data leader based in France. We chat about how she helps large organizations drive change, especially around data architecture and strategy. We dig into her approach to upskilling teams, why data strategy matters, and how to work through the inevitable organizational challenges that come with transformation. We also discuss the AI in learning and work, and some interesting differences in education between USA and France. Enjoy! 🇫🇷

In this episode, we rewind to one of biology’s biggest plot twists: RNA interference (RNAi). Scientists found that injecting double-stranded RNA into Caenorhabditis elegans could silence genes powerfully and precisely—far beyond anything single strands could achieve.

This game-changing discovery revealed:

How dsRNA triggers targeted gene shutdown Why only a few molecules can silence thousands of cells How gene silencing spreads across tissues The first clues toward RNA-based therapies that would change medicine forever

📖 Based on the research article: “Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans” Andrew Fire, SiQun Xu, Mary K. Montgomery, Steven A. Kostas, Samuel E. Driver & Craig C. Mello. Published in Nature (1998). 🔗 https://doi.org/10.1038/35888

🎧 Subscribe to the WoRM Podcast for more whole-organism stories that changed the world!

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]

A practical guide to reproducible and high impact mass spectrometry data analysis R Programming for Mass Spectrometry teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. Readers will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. Key topics include: Comprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data Processing chromatographic peaks, peak detection, and quality control in mass spectrometry data Applying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights Methods for producing reproducible, publication-ready reports and web pages using RMarkdown R Programming for Mass Spectrometry is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry.

Pitch by participating AI startup

Break into data analytics EVEN without a degree, just like our guest for today's episode! He's Ryan Ponder, a Data Analytics Accelerator program student who transitioned from Loan Officer to Data Analyst within his company-- without a degree. He shares how he leveraged internal opportunities and attained his new role. Tune in and learn actionable steps for making an internal pivot and overcoming career challenges! 💌 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 05:49 - The Internal Pivot: A Unique Pathway to Data Analytics 09:57 - Networking and Overcoming Challenges 15:47 - Imposter Syndrome 23:02 - Final Thoughts and Advice 🔗 CONNECT WITH RYAN 🤝 LinkedIn: https://www.linkedin.com/in/rtponder/ 🔗 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

Today, I'm chatting with Shri Santhanam, the  EVP of Software Platforms and Chief AI Officer of Experian North America. Over the course of this promoted episode, you’re going to hear us talk about what it takes to build useful consumer and B2B AI products. Shri explains how Experian structures their AI product teams, the company’s approach prioritizing its initiatives, and what it takes to get their AI solutions out the door. We also get into the nuances of building trust with probabilistic AI tools and the absolutely critical role of UX in end user adoption.

Note: today’s episode is one of my rare Promoted Episodes. Please help support the show by visiting Experian’s links below:

Links

Shri's LinkedIn Experian Assistant | Experian Experian Ascend Platform™ | Experian 

A jargon-free primer on GIS concepts and the essential tech tools Geographic Information Systems (GIS) is the fascinating technology field that's all about understanding and visualizing our world. GIS For Dummies introduces you to the essential skills you'll need if you want to become a geospatial data guru. You'll learn to read, analyze, and interpret maps, and you'll discover how GIS professionals create digital models of landscapes, cities, weather patterns, and beyond. Understand how advances in technology, including AI, are turning GIS tools into powerful assets for solving real-world problems and protecting the planet. This beginner-friendly book makes it easy to grasp necessary GIS concepts so you can apply GIS in your organization, pursue a career in this dynamic field, or just impress others with your geographic knowledge. Learn the basics of data analysis, interpretation, and modeling using Geographic Information Systems Gain the skills to read and interpret all types of maps and visual GIS information Discover how GIS is used in fields like urban planning, environmental science, business, and disaster management Explore whether a career in GIS could be right for you GIS For Dummies is the perfect starting point for students, professionals, and anyone curious about the potential of GIS as a technology or career choice.

Today, we’re joined by Ted Elliott, Chief Executive Officer of Copado, the leader in AI-powered DevOps for business applications. We talk about:  Impacts of AI agents over the next 5 yearsTed’s AI-generated Dr. Seuss book based on walks with his dogThe power of small data with AI, despite many believing more data is the answerThe challenge of being disciplined to enter only good dataGaming out SaaS company ideas with AI, such as a virtual venture capitalist

AI maturity isn't achieved through technology alone—it requires organizational alignment, cultural readiness, and strategic implementation. Companies across industries are working to move beyond experimental AI use toward systematic integration that delivers measurable business value. How do you assess where your organization stands on the AI maturity spectrum? What frameworks can help prioritize your efforts? Eryn Peters, Co-founder & co-creator at AI Maturity Index, is a future of work evangelist. She is the co-creator of a tool for assessing AI maturity, and regularly advises companies on how to assess and improve their AI maturity. Eryn is also the Editor of the Weekly Workforce newsletter and the Principal at the Startup Consortium consultancy. Previously, she was the Global Director of the Association for the Future of Work, and VP of Marketing at Andela. Iwo Szapar is a serial entrepreneur with a passion for creating impactful solutions that enable people to work smarter, not harder. He is the co-founder of several innovative initiatives, including Remote-how, Remote-First Institute, AI-Mentor, and the Saudi AI Leadership Forum. Throughout his career, Iwo has helped transform how over 3,000 companies—including Microsoft, Walmart, and ING Bank—approach the future of work. In the episode, Richie, Eryn, and Iwo explore AI maturity in organizations, the balance between top-down and bottom-up AI adoption, the relationship between data and AI maturity, the importance of change management, practical steps for AI implementation, and much more. Links Mentioned in the Show: AI Maturity IndexEryn’s WebsiteIwo’s Book: Remote Work Is The WayConnect with Eryn and IwoState of Data & AI Literacy Report 2025Eryn’s previous webinar: Assessing Your Organization's AI MaturityRelated Episode: Scaling Responsible AI Literacy with Uthman Ali, Global Head of Responsible AI at BPRewatch sessions from RADAR: Skills Edition 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

Ganesh Kumar Suresh: Agile Meets Data Science: Agile Strategies for High-Impact Data Science & Machine Learning Projects

Discover all-practical implementations of the key algorithms and models for handling unlabeled data. Full of case studies demonstrating how to apply each technique to real-world problems. In Data Without Labels you’ll learn: Fundamental building blocks and concepts of machine learning and unsupervised learning Data cleaning for structured and unstructured data like text and images Clustering algorithms like K-means, hierarchical clustering, DBSCAN, Gaussian Mixture Models, and Spectral clustering Dimensionality reduction methods like Principal Component Analysis (PCA), SVD, Multidimensional scaling, and t-SNE Association rule algorithms like aPriori, ECLAT, SPADE Unsupervised time series clustering, Gaussian Mixture models, and statistical methods Building neural networks such as GANs and autoencoders Dimensionality reduction methods like Principal Component Analysis and multidimensional scaling Association rule algorithms like aPriori, ECLAT, and SPADE Working with Python tools and libraries like sci-kit learn, numpy, Pandas, matplotlib, Seaborn, Keras, TensorFlow, and Flask How to interpret the results of unsupervised learning Choosing the right algorithm for your problem Deploying unsupervised learning to production Maintenance and refresh of an ML solution Data Without Labels introduces mathematical techniques, key algorithms, and Python implementations that will help you build machine learning models for unannotated data. You’ll discover hands-off and unsupervised machine learning approaches that can still untangle raw, real-world datasets and support sound strategic decisions for your business. Don’t get bogged down in theory—the book bridges the gap between complex math and practical Python implementations, covering end-to-end model development all the way through to production deployment. You’ll discover the business use cases for machine learning and unsupervised learning, and access insightful research papers to complete your knowledge. About the Technology Generative AI, predictive algorithms, fraud detection, and many other analysis tasks rely on cheap and plentiful unlabeled data. Machine learning on data without labels—or unsupervised learning—turns raw text, images, and numbers into insights about your customers, accurate computer vision, and high-quality datasets for training AI models. This book will show you how. About the Book Data Without Labels is a comprehensive guide to unsupervised learning, offering a deep dive into its mathematical foundations, algorithms, and practical applications. It presents practical examples from retail, aviation, and banking using fully annotated Python code. You’ll explore core techniques like clustering and dimensionality reduction along with advanced topics like autoencoders and GANs. As you go, you’ll learn where to apply unsupervised learning in business applications and discover how to develop your own machine learning models end-to-end. What's Inside Master unsupervised learning algorithms Real-world business applications Curate AI training datasets Explore autoencoders and GANs applications About the Reader Intended for data science professionals. Assumes knowledge of Python and basic machine learning. About the Author Vaibhav Verdhan is a seasoned data science professional with extensive experience working on data science projects in a large pharmaceutical company. Quotes An invaluable resource for anyone navigating the complexities of unsupervised learning. A must-have. - Ganna Pogrebna, The Alan Turing Institute Empowers the reader to unlock the hidden potential within their data. - Sonny Shergill, Astra Zeneca A must-have for teams working with unstructured data. Cuts through the fog of theory ili Explains the theory and delivers practical solutions. - Leonardo Gomes da Silva, onGRID Sports Technology The Bible for unsupervised learning! Full of real-world applications, clear explanations, and excellent Python implementations. - Gary Bake, Falconhurst Technologies

In this episode, Tristan talks to Zach Lloyd, founder of Warp—a terminal built for the modern era, including for AI agents. They explore the history of terminals, differences between terminals and shells, and what the future might look like. In a world driven by generative AI, the terminal could once again be the control center of computer usage. For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com. The Analytics Engineering Podcast is sponsored by dbt Labs.

Intel-hosted series of 6 sessions focused on agentic AI learning.