The article examines the emerging data science roles that have surfaced following the Large Language Model boom, focusing on AI Engineers, Applied Scientists, and Forward Deployed Engineers. It explores how these positions differ and what responsibilities they entail in the current AI landscape. The piece provides an overview of each role’s unique contributions to the development and deployment of AI technologies.
What actually makes a Forward Deployed Engineer, told through one supply chain project. The post The AI Was the Easy Part: What Is a Forward-Deployed Engineer in a Supply Chain?
AI has accelerated data scientists’ productivity, but its influence extends beyond speed. The technology is reshaping who owns data, how judgment is exercised, and the overall career trajectory of data scientists. These changes signal a broader transformation in the field’s structure and responsibilities.
How AI has massively changed my day-to-day workflow The post A Day in the Life of a Data Scientist in 2026 appeared first on Towards Data Science .
Towards Data Science has released a video showcase titled "Introducing ShipAI," which highlights real‑world AI work. The post announces this new visual resource and its focus on practical AI applications. It is positioned as a first look into the platform’s capabilities.
The article titled "5 AI Skills That Will Keep Data Scientists Relevant in 2027" outlines five specific AI competencies, explaining what each skill addresses and providing runnable code snippets that readers can directly paste into a notebook. It serves as a practical guide for data scientists aiming to stay current with emerging AI technologies.
The article titled "An Introduction to Jev" discusses an AI system that focuses on making decisions rather than generating text. It highlights Jev’s unique approach to decision-making within the broader context of AI development. The piece was originally published on Towards Data Science.
The article discusses how engineers should view the lack of benefit from current AI tools as a finding rather than a failure. It encourages documenting these observations, sharing them with peers and managers, and actively seeking solutions. By explaining where AI falls short, engineers demonstrate the judgment expected of them.
They aren’t designed, you can’t help perceiving one anyway, and that makes them an engineering problem almost no one is solving. The post Where Does an AI’s Personality Actually Come From?
The article titled "10 Things I’m Learning Beyond AI to Become More Technologically Fluent" discusses the author's exploration of various technologies that are shaping the future, beyond just artificial intelligence. It is presented as part one of a series, focusing on understanding these emerging technologies and their impact.
The article "A Scaffold at the Gate: Building AI Applications That Can Evolve" reflects on the necessity for data scientists to adopt software engineering principles in the context of AI agents that generate code. It discusses how these principles can help create AI applications capable of evolving over time. The piece was originally published on Towards Data Science.
How one open-source ecosystem made state-of-the-art AI accessible The post The Python Ecosystem That Changed AI Development appeared first on Towards Data Science .
The article lists four skills that data scientists should acquire to stay competitive in 2026. It emphasizes the importance of integrating these skills into current workflows to avoid falling behind. The post was originally published on Towards Data Science.