Never miss a new edition of The Variable, our weekly newsletter featuring a top-notch selection of editors’ picks, deep dives, community news, and more.
The debate around AI’s impact on tech careers has been polarizing—to put it very, very mildly.
The utopians are pointing towards a future where data scientists and programmers can focus on management, strategy, and deep thinking, instead of on boring, repetitive tasks. The pessimists, meanwhile, are dreading a future in which there are no more data scientists and programmers.
This week, we invite you to explore the space between these positions and the opportunities that arise amid uncertainty. The articles we’ve selected suggest that we can harness AI’s power to become better and more effective at our jobs—while foregrounding the qualities that make humans irreplaceable.
Become a Better Data Scientist with These Prompt Engineering Tips and Tricks
“I see prompt engineering as a superpower,” says Sara Nobrega—one that enables smarter work and substantial time savings for junior and seasoned data professionals alike. In the first part of her new series, Sara unpacks the benefits of prompt engineering during the EDA (exploratory data analysis) process.
Rethinking Data Science Interviews in the Age of AI
Yu Dong makes a compelling case for an AI-informed hiring process, and explains how candidates can use new tools to showcase their skills.
Your Personal Analytics Toolbox
With the aid of the open-source MCP (model context protocol), Mariya Mansurova believes data scientists stand to make their work more streamlined—and more interesting.
This Week’s Must-Read Stories
Catch up on the articles our community has been buzzing about in recent days:
- Lessons Learned After 6.5 Years Of Machine Learning, by Pascal Janetzky
Covering deep work, trends, data, and research.
- Software Engineering in the LLM Era, by Stephanie Kirmer
On growing new software engineers, even when it’s inefficient.
- An Introduction to Remote Model Context Protocol Servers, by Thomas Reid
How to write, test, and use them effectively.
Other Recommended Reads
Explore a few more standout articles we published recently — they cover timely topics like bias in LLMs, scalable AI, and freelancing as a data scientist:
- Stop Chasing “Efficiency AI.” The Real Value Is in “Opportunity AI.”, by Shreshth Sharma
Companies pursuing incremental productivity gains risk being displaced by AI-native competitors building entirely new business models.
- A Developer’s Guide to Building Scalable AI: Workflows vs Agents, by Hailey Quach
Understanding the architectural trade-offs between autonomous agents and orchestrated workflows.
- What I Learned in my First 18 Months as a Freelance Data Scientist, by CJ Sullivan
The taxes and health insurance edition.
- Fairness Pruning: Precision Surgery to Reduce Bias in LLMs, by Pere Martra
From unjustified shootings to neutral stories: how to fix toxic narratives with selective pruning.
- Data Science: From School to Work, Part V, by Vincent Margot
A popular series comes to a close with a guide to profiling your Python project.
Meet Our New Authors
Discover top-notch work from some of our recently added contributors:
- Dave Flynn‘s first TDS article focuses on change-aware data validation.
- Jens Winkelmann joins our author community with a multidisciplinary background in physics, data science, and AI.
- Ashton Gribble dedicates his debut story to the algorithm powering song-identification app Shazam.
We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?
