If you are a Data Scientist, you have never had more alpha than right now. Data Scientists email me all the time. It is no mistake that I'm focused on evals as a former DS. I talk more about this here:

Former $META data scientist George Volichenko testified that just 0.2% of teens used Instagram’s “Take a Break” feature when it was opt-in. Meta has since made the feature enabled by default for teen accounts.

I’m hiring an economist / economics-leaning data scientist to join Ramp Economics Lab. As AI adoption is approaches 100%, measuring whether businesses use AI is no longer enough. We need new metrics that show how deeply companies are using it, where it creates value, and how AI is changing the economy. I'm looking for someone full-stack. You will: 1) Develop independent research and build new metrics for Ramp AI Index. 2) Build and design those metrics directly onto our website (we vibecode it ourselves) 3) Present our research externally, on-site with customers or in-person events. Send your top recommendations!

In 2013, data analysis at Wayfair was trapped on on-premise SQL Server boxes that couldn't even join across databases. Over the following decade, cloud data warehouses and the modern data stack tore down constraint after constraint, turning data scientists from reporters into operational decision-makers. 📈 Now AI has made producing an analysis nearly free. But in "The Shape and Feel of the Post-AI Data Stack," Ian Macomber argues that agreeing on reality never gets cheaper. The more dashboards proliferate, the more likely different interfaces return different answers to the same question — a growing "consensus divergence" problem. 🤖 The post-AI data stack Macomber describes puts an agent harness between infrastructure and people: agents parse data products and reassemble insights for humans, not the other way around. That demands four things — artifacts agents can read, tools agents can operate via API, context that stays agent-agnostic across vendors, and consensus that can be systematically tested. Ramp, he notes, fires identical questions through every interface and measures how often the answers diverge, aiming for zero. ✍️ Macomber's conclusion: a data scientist's worth will be measured not by any single analysis, but by how well their judgment gets encoded into infrastructure so every future agent, decision, and employee inherits what's true and why. URL: #DataStack# #AIAgents#

AI you can trust starts with questions. AT&T data scientist Natalie Gilbert puts skepticism to work by thinking through edge cases and making informed decisions instead of blindly trusting AI.

"Active customer" can mean 3 different things inside the same company. At Canva, Sam Redfern is teaching AI which one you mean. Sam is Staff Data Scientist at Canva focused on enabling AI data science at scale across all of Canva's data teams. On Nov. 5 at Data Outpost, Sam will share semantic leaking, a technique developed at Canva that uses LLMs to extract business language from technical artefacts and feed it into the semantic layer. Ask your AI analytics tool a question your data can't answer, and see whether it admits it. Then come hear how Sam tests for exactly that. 🎟️ Tickets:

excited to share that @chrisellisdc just raised $108M for @thatch_ai to build a healthcare system people love he's hiring across the board: - Head of Design - Data Scientist, Analytics - Software Engineers - Growth Marketing Lead - SEO & Performance Content Lead - Video Creative - Product Manager - Channel Partnerships Manager - Product Designer - Operations Manager apply here: