Data projects: Are we doing science or engineering? linkedin.com/feed/update/ur…

Science is about creating new knowledge. Generating and testing new explanations. In data science, this requires both domain expertise and data skills. Without domain expertise, you can't generate good explanations. Without data skills, you can't test them.

Are you selling to enterprises? How many times are you asked about IP, freedom to operate, terms, team, certificates of insurance, bank letters, ultimate ownership, conflicts of interest statements... Have you used a data room for enterprise deals before?

Anyone using GPTChat for data cleaning and transformations?

Why do ML projects fail? It’s not usually about technology. It’s about people. 👩🏻👦🏽👨🏼‍🦲🧑🏾‍🦳👩🏿‍🦱👨🏼👱🏾👱🏽‍♂️👩🏼‍🦳👨🏿‍🦳👩🏼‍🦲🧑🏾‍🦲🧔🏻‍♂️ First, let’s talk about successful ML projects. Almost invariably, they bring together a group of people with different but overlapping expertise: <1/13>

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90% of you will read this post, bookmark it, and forget about it immediately. But more bookmarks won't help you become a better data scientist. To make progress, you must do something different. Here is one idea: humyn.ai is a platform where you can work on real projects for

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Humans are funny. Preaching big vision. Sweating small stuff. I talk a lot about abundance. It’s how we help companies solve the AI talent crisis in a novel way at Humyn-ai. And yet, I catch myself agonizing over whether I really need one more $19/month SaaS tool.

What is a tree-shaped engineer?! 🌳 Merelda Wu dropped this idea in our conversation and I haven’t stopped thinking about it. It’s one of the best ways I’ve heard to think about building great dynamics on AI teams. Watch the clip, maybe you'll start growing a forest.

Do you have to be tri-lingual to lead an AI team? You've got to balance 3 things, says Merelda Wu: business goals, experimental science, and production‑grade engineering. Great leaders speak all three languages and translate in real time.

What's the best operating model for AI? Should GenAI be in its own team forever? Peter Gostev argues “probably not.” Right now the tech is changing too fast, so a dedicated team is the only way to go. But once the tech stabilizes, expect GenAI to collapse back into engineering.

Good AI leaders don’t just delegate; they prototype. According to Peter Gostev, it's critical to work hands-on with the latest AI models. How else can you know what's possible and which use cases are worth building?

Why did this Head of Data Science refuse to lead projects? When Adrienne Leonard was Head of Data Science at a major aviation technology firm, she insisted that her team would never 'lead' a project. Here's why:

Constantly hearing about the AI talent shortage? Maybe we are missing something right under our noses? Sam Fletcher’s team uncovered 51% more internal data science skills. Result: a clear map of previously hidden skills and internal mobility opportunities that most overlook.

AI leaders: want to make stronger project pitches and run fewer dead-end pilots? In this 60-second clip, Kristina Kaganer reveals her go-to “sparring partner”: a personal board of LLMs. Before she puts forward an idea, she asks ChatGPT, Gemini, and Claude to rip it apart.

Could you accelerate your leadership career with a team of specialist advisors? In this short clip, Kristina Kaganer shares how she created an always-on “LLM advisory board.”