How does a data scientist stay relevant in an era where frontier capabilities and best application practices change weekly? This was the central theme of a recent Royal Statistical Society (RSS) event held at UCL, featuring Piers Stobbs (Cofounder of Epic Life) and Giles Pavey (Unilever and UCL).
Drawing on his extensive career leading teams at Deliveroo, Cazoo, Moneysupermarket, and dunnhumby, Piers shared a practical, actionable system for information management and learning. He explained that, in a world flooded with automated summaries and endless content, staying informed depends on focusing your attention on well-curated insights and thoughtful perspectives from trusted experts. Curating the DelugeThe “information firehose” is no longer just a metaphor; it is a structural challenge for the profession. Piers illustrated this by contrasting the section’s first newsletter in February 2020—a “short and sweet” dispatch—with the April 2026 edition covering over 100 useful links. The newsletter has grown significantly in both length and complexity, reflecting a field that now spans deep and varied research, complex ethical and governance considerations, an increasingly broad set of core best practices and a dizzying array of generative AI applications.
The problem, Piers argued, is that the sheer volume of “must-read” content creates a paradox of choice. Practitioners often find themselves oscillating between total information overload or retreating into a narrow silo, missing the big picture shifts that define career longevity. The challenge lies in filtering the content you need without losing the essential context.
The “Tag and Batch” Heuristic
Rather than reacting to every notification, Piers advocated for a “batch processing” approach to professional development.
The System: Piers uses raindrop.io to store relevant bookmarks in a monthly repository for the newsletter. Each bookmark is added to a specific monthly collection, and also includes a tag for the newsletter topic it is relevant for. The bookmarks are then pulled together into a coherent narrative once, at the end of the month.
Curation: Once a week, he performs quick scans of various trusted sources and newsletters he has built up over the years—such as TLDR (AI and Data) for technical updates and The Batch for curated commentary—saving interesting links without immediately diving deep. He advocates for a brief high level assessment at this stage to avoid going down rabbit holes- the risk is spending an unsustainable amount of time in this stage.
Assimilation: Then, once a month, he calls up the saved bookmarks and compiles them into a coherent story. This can be time consuming but is very worthwhile in terms of understanding. He starts with the first topic, opening up all the relevant links, and thinks through how they are related, how important they are and how best to position them from a narrative perspective. Once the first topic is done, he continues through the rest of the topics, finally pulling out the “top 5 must reads” for the headline links at the top of the newsletter.
The Benefit: By tagging articles by section (e.g., Ethics, Engineering, Big Picture) at the point of discovery, the final synthesis becomes a task of finding themes rather than hunting for links. And the deeper dive learning happens in a consolidated period once a month rather than spread in an expanding amount of time throughout the month.
Diversity of Signals
Piers and Giles reccomend compiling a specific toolkit of sources designed to cover the full spectrum of data science, rather than just the latest GenAI hype:
Latest Updates: TLDR and The Batch are used to make sure the latest updates and developments are not missed.
Technical Foundations: Data Science Weekly is utilised to ensure a focus on “traditional” ML and statistics remains alongside newer developments.
The “So What?” Factor: Strategists like Benedict Evans and Azeem Azhar provide the macro-view, while Ethan Mollick offers grounded takes on how these models actually apply to real-world tasks.
The Future Lens: Import AI by Jack Clark serves as a window into the actual capabilities and future risks of frontier models.
No shortcuts to understanding
One of the most provocative points of the evening was the argument against full automation.
The Risk: Tools like NotebookLM can generate impressive summaries or even AI podcasts from source materials: Piers’ showed an impressive set of slides created from the 11 newsletters released in 2025. However, he warned that automating the full curation and assimilation process risks losing the understanding that is generated through the effort of review. Some “grit” in the process is key for true learning.
The Heuristic: The process of reading, categorising, and storytelling is what facilitates learning. If the digest is fully automated, the practitioner loses the ability to synthesise original insights and spot subtle, evolving patterns across different domains. Automation can definitely help- but care should be taken to make sure the learnings are not automated away.
Evals and Breadth
The evening concluded with a spirited Q&A that touched on the future of the role of a data scientist. When asked how early-career data scientists should prepare for a world where AI automates code, Piers’ advice was twofold: evals and breadth.
With AI making output generation increasingly easy (whether it is applications or analytical output), the critical question becomes whether or not the output is any good. A crucial capability all data scientists need is the ability to evaluate the output. How do we measure how good it is? How do we build processes that allow us to keep track of this quality measurement? How do we understand whether or not changes we make, cause the system to actually improve?
Separately, AI allows individual engineers, data scientists (and product managers and designers) to do more things. So exploring and expanding your capabilities across different areas - giving yourself more breadth - allows you to stand out if there is increasing downward pressure on team sizes and hiring.
Source Toolkit
Workflow Tool: Raindrop.io for bookmarking and tagging.
The Evolution: Compare our Feb 2020 debut with our April 2026 edition.
Reading List:
TLDR (AI and Data newsletters);
The Batch from Andrew Ng;
Import AI from Jack Clark;
One Useful Thing from Ethan Mollick;
Exponential View from Azeem Azhar.
If you have found this helpful, make sure to check out the newsletter here.
Copyright and licence : © 2026 Piers Stobbs 
This article is licensed under a Creative Commons Attribution 4.0 (CC BY 4.0) International licence.
How to cite :
Piers Stobbs 2026. “How to stay up to date as a data scientist?” Real World Data Science, 2026. URL