The Data Science Identity Crisis | Anurag Gangal (Spotify) on Data Roles, Analytics Engineering & AI

What does a data scientist actually do anymore?

In this episode of the Stacked Data Podcast, Harry sits down with Anurag Gangal from Spotify to unpack one of the biggest challenges in modern data: the growing confusion around data role titles.

From data scientists and analytics engineers to product analysts, machine learning engineers, and more, the data landscape has become increasingly hard to navigate. Anurag shares the story behind his framework for understanding data roles, why he built his now-popular quadrant model, and how it can help both companies and individuals make better decisions.

The "Data Scientist" identity crisis - Anurag’s Substack

They explore why so many businesses still use the title data scientist to describe completely different jobs, how that creates problems in hiring and team design, and what it means for people trying to build careers in data. The conversation also dives into generalists vs specialists, the evolution of the modern data stack, and how AI could reshape the future of analytics, data science, and self-serve data work.

Whether you’re a data leader, analytics engineer, data analyst, product analyst, machine learning engineer, or someone trying to break into data, this episode will help you better understand where the industry is heading.

In this episode, we cover:

• Why the term data scientist has become so confusing
• The difference between analytics engineers, data analysts, product analysts, and ML engineers
• How to think about specialisation vs generalisation in data teams
• The real cost of poorly defined data roles
• How Anurag’s data role quadrant model helps bring clarity
• How to think about your career path in data
• How AI may change the future of data science, analytics engineering, and self-serve analytics

Guest: Anurag Gangal, Spotify
Host: Harry Gollop
Podcast: Stacked Data Podcast

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Our sponsor is Omni, an AI-powered BI platform that helps people use data to do their best work. Whether users prefer AI, Excel, point-and-click exploration, or SQL, Omni enables fast, trusted answers from a governed semantic model.

The Stacked Data Podcast is produced by Cognify — a specialist recruitment partner for teams working across the modern data stack, machine learning & AI.

If you’re looking to hire top data talent or exploring your next move in data, feel free to reach out to the Cognify team — we’re always happy to help and chat through the market.

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