Delphina

Do executives want data scientists to spend their days: A) Answering the questions they've been asked to answer B) Figuring out what questions to ask, and answering those instead When I talk to data scientists, they far and away hope for the latter. But is that the reality for what executives expect? We probably all have a sinking feeling that (A) is more common... Why? We get into this and much more with Gabriel Weintraub, Professor at Stanford University Graduate School of Business, and advisor to companies small and large including Airbnb, AppNexus, and Outschool on the latest episode of High Signal, hosted by Hugo Bowne-Anderson. Gabriel points out that many teams have real work to do to establish themselves as true custodians of the business. Far too often, folks get excited about the data itself (and other technical challenges) instead of what the business really cares about, and when that happens it's easily felt by business leaders, and instantly erodes the trust needed for independence. The best teams are hyper-aligned with business objectives (which are often fast changing!). Fun fact: I first met Gabriel back in 2017 when he moderated a panel I was on. At the time I was asking myself, what I am doing being interviewed by this guy — he should be the one giving the answers! So it was fun here to finally turn the tables. Check out the full episode:  1/ Spotify: https://lnkd.in/gPR4Q3KD 2/ Apple Podcasts: https://lnkd.in/gTSQFJMQ 3/ YouTube: https://lnkd.in/gAkzuuCu Please let us know what you think! And if you like it, don’t forget to hit the like button and subscribe.

How can data science drive strategic impact, deliver transformative products, and shape leadership careers? 💫 On Thursday, December 5, at 12 PM ET, I’ll be hosting Geetu Ambwani—a seasoned leader in data science and machine learning—for the next Data Dialogs session with Duncan Gilchrist and Delphina. Geetu has led data teams and initiatives across healthcare and media, including roles at Spring Health, Flatiron Health, and HuffPost, and we'll be talking about: 💡 Positioning Data Science as a Strategic Function Shifting from a support role to a strategic partner driving innovation and business value. 🚀 Building Impactful Data Products Designing products that deliver measurable results, especially in the era of generative AI. ⭐ Career Pathways for Data Leaders Navigating senior roles, from executive leadership to entrepreneurial ventures. This off-the-record, application-only session is built for data leaders to discuss real-world challenges and solutions. 📅 When: Thursday, Dec 5 | 12 PM ET 🔗 Apply to join: https://lnkd.in/dBZgzHHZ Looking forward to learning alongside a community of data science leaders. Let’s tackle the big questions together. 🫶

💭 Does your leadership ask for AI solutions before your team even has access to clean, reliable data? Too often, companies jump into AI without a clear plan, leading to wasted effort and frustration. In this episode of High Signal, I talk to Gabriel Weintraub, the Amman Mineral Professor at Stanford Graduate School of Business, about the steps organizations need to take to build successful data-driven cultures. From foundational strategies to experimentation and local innovation, we cover: 📊 Building Foundations Before AI Why starting with reliable data and high-ROI, low-complexity projects is key to sustainable AI success. 🤝 Closing the Gap Between Leadership and Data Teams How collaboration between executives and technical teams can align priorities and solve real business problems. 🔬 Experimentation as a Cultural Shift Why even negative results are valuable—and how organizations can normalize experimentation for better learning and outcomes. 🌍 The Role of Startups and Local Innovation Gabriel highlights Latin America’s untapped potential in AI and the importance of fostering talent and building local solutions. This podcast is produced by Delphina, with Duncan Gilchrist and Jeremy Hermann, and is designed to help you navigate the complex world of AI, data science, and machine learning while advancing your career. 🎬 Watch the clip here to hear Gabriel explain why starting with the basics is critical for AI success. Check out the podcast on wherever you like to listen, and let us know what you think! * Apple: https://lnkd.in/gVC73DxC * Spotify: https://lnkd.in/gVi6a4Ch * Youtube: https://lnkd.in/gkqF55bR * More episodes and show notes: https://lnkd.in/gjnygZ4G

Our latest edition of #BuilderOpsBlueprints is a twofer with the founders of Delphina, Jeremy Hermann and Duncan Gilchrist! Delphina harnesses generative AI to automate foundational data science tasks, uncovering and cleaning relevant data to ultimately transform noise to meaningful signal. In our Q&A, Jeremy and Duncan dive into their customer discovery process, the secrets to great cofounder dynamics, their new data science podcast 𝘏𝘪𝘨𝘩 𝘚𝘪𝘨𝘯𝘢𝘭, and practical advice for early-stage startups. P.S. If you’re interested in working at the edge of machine learning and AI, check out open roles 👉 https://lnkd.in/g7iVpWAj -- #BuilderOpsBlueprints is a newsletter on company building foundations for early-stage startups by the BuilderOps team at Costanoa Ventures, highlighting learnings from founders and startup leaders. Make sure to hit the subscribe button!➕📫 #founders #startups #datascience #artificialintelligence #machinelearning

Can’t wait to welcome Geetu Ambwani as expert guest at our next Data Dialog! Geetu has rich DS leadership experience across health and media verticals, as former VP of DS&ML at Spring Health, Sr Director at Flatiron Health, and head of DS&ML at HuffPost. We’re going to discuss topics including 1. 💡 Data Science as a Strategic Function 2. 🚀 Building Impactful Data Products 3. ⭐️ Career Pathways for Data Leaders The Data Dialogs are an off-the-record, application-only, virtual forum for data science leaders to get real about the problems they face. Coming up with Hugo Bowne-Anderson, in 2 weeks on Thursday 12/5 at 9am PT / 12 pm ET. Find out more and apply to join an incredible community of Data Leaders: https://lnkd.in/gywPgDte

I was extraordinarily lucky in my first role at Uber to fall into a job building and leading the Marketplace Experimention team. At the time I was actually a little bummed — I had joined Uber to work on surge pricing, which seemed objectively more exciting — but it’s since become obvious to me what a springboard that first job was. It was critical in setting me up for a variety of bigger roles leading pricing and marketplace optimization science thereafter. That’s because something unique and under-appreciated about working on experimentation is the extreme variety of problems and people you get exposure to. Even as an IC, you get to see what all the different product teams are working on, who their stakeholders are, and what works and what doesn’t. You’re able to instantly get a global view of the business and be part of many conversations that you don’t really deserve to be in. Ramesh Johari, Professor at Stanford and advisor up and down the valley to companies like Stripe & Airbnb, exemplifies this breadth to the highest degree — he “gets it” in a way very few in data science do, both because he's technically brilliant, yet also because he's simply seen so much advising different experimentation teams. He is truly bilingual in greek letters and c-suite english. I was fortunate to be able to recruit Ramesh to advise my team at Uber and we’ve stayed in touch since. Ramesh joined Hugo Bowne-Anderson on our latest episode of High Signal to share his experimentation playbook, honed over the years. It's a punchy episode, full of things you should be doing that I found myself jotting down live. One piece he stresses is how much experimentation is not about individual tests, but rather about evolving organizational understanding; the best organizations see experimentation as a means to understand the world. I couldn’t agree more. Check out the clip here and the podcast on wherever you like to listen, and let us know what you think! * Apple: https://lnkd.in/gTSQFJMQ * Spotify: https://lnkd.in/g3b4hugi * Youtube: https://lnkd.in/gmDgDc6d * More episodes and show notes: https://lnkd.in/gMKJUXFY #datascience #artificialintelligence #leadership

Are you looking to deepen your understanding of data science and machine learning? If so, I’m thrilled to share the podcast High Signal, hosted by my friends Hugo Bowne-Anderson, Duncan Gilchrist, and Jeremy Hermann! Check it out here – https://bit.ly/hssvp 🎙️ What to expect: – In-depth Interviews with industry leaders and academics such as Andrew Gelman (Columbia University) and Chiara Farronato (Harvard Business School) and that’s just the start! – Learn from the best that you can apply directly to your work – Strategies for Bridging the Gap between technical and non-technical teams – Case Studies showcasing real-world applications of data science Whether you're a data scientist, engineer, or business leader, High Signal offers valuable perspectives to help you thrive in your field. Upcoming episodes will feature: 🏆 Guido Imbens (Nobel Laureate), Hilary Mason, Emily Sands, Ramesh Johari, and many more. New episodes are released twice a month! Subscribe now on Spotify or Apple Podcasts to stay informed. Tune in today and get ready to elevate your understanding of the data landscape! #data #ai #podcast #delphina #theravitshow

Thrilled to have the polymathic Noah Smith (who reaches 300k readers at his substack Noahpinion) on the main stage yesterday at National Association for Business Economics (NABE) TEC! Noah walked us through how improvements in  1/ solar power 2/ batteries  3/ AI  form a triad of extremely complementary innovations. You've probably heard that because of scientific breakthroughs and scale, costs of both solar power and batteries have fallen by an order of magnitude in the last decade, while battery density has doubled in the same time. What you might not have realized is that there’s an incredible feedback loop there: the biggest problem with solar power is it’s only available when it’s sunny. Except that’s not a problem with vastly better battery tech. And more scale in one drives scale in the other which further drives down cost. It’s hard to overstate how big a deal this is. Put that together with recent advances in AI, and we now we have * cheap * portable * clean energy * with a brain. We see real learning curves in all of these areas — it’s only going to get cheaper, more portable, and more intelligent. What can go right.. or go wrong? 🤖🤖🤖