Over the past months, Claude Code has become a crucial part of my day-to-day work as a data scientist. While I remain skeptical about the long-term effects of AI assistants on attention spans and our ability to think slowly and deeply, the practical demands of my current job are clear: I need to produce more code, across a wider range of projects, and to do so reliably — we are currently two people short in my team and projects have tight deadlines. In that context, Claude Code has proven to be an exceptionally effective tool.
What matters, however, is how it is used. Over time, I’ve noticed three recurring use cases, each highlighting different aspects of technical judgment, system understanding, and responsibility.
Working outside my comfort zone / expertise
The most challenging use cases are those where my understanding of the problem domain is incomplete. At the moment, our team is without a dedicated Cloud Engineer, so I regularly find myself debugging Kubernetes workloads and Terraform configurations. I’m comfortable with kubectl and our infrastructure codebase, but some architectural details inevitably sit outside my core expertise.
In these situations, Claude Code is highly effective at narrowing down root causes, suggesting sensible debugging steps, and proposing potential fixes. This is also where the risk is highest: solutions often involve trial and error, and blindly applying changes would be irresponsible.
For that reason, I treat Claude Code as a diagnostic partner rather than an autonomous agent. I implement changes myself, question any suggestion that feels brittle or opaque, and avoid “fixes” I can’t explain afterwards. This approach has accelerated my learning curve substantially — not only resolving immediate issues, but deepening my understanding of our infrastructure.
Paradoxically, this experience also strengthens my ability to collaborate with specialists. I’m confident it will make me a more effective counterpart — and later, a better manager — for our incoming Cloud Engineer, particularly during onboarding.
One-Off Tools and Exploratory Scripts
At the other end of the spectrum are short-lived, tactical tools. A recent example involved authentication issues with the Tableau Server API. Another team was setting up an integration and struggled to establish a working connection.
Traditionally, I would have reached for Postman Hoppscotch, manually tested endpoints, and iterated on credentials and headers. Instead, I created a temporary directory and prompted Claude Code to:
Create a Python-based test harness for the API described in these documentation pages. Credentials should be stored in an
.envfile. Use modular classes for different parts of the API.
The result was a small but functional test framework that allowed me to quickly simulate different scenarios. While I didn’t review every line of generated code in depth, it enabled me to isolate the real problem efficiently — which ultimately turned out to be unrelated to the API endpoints themselves.
Once the issue was resolved, I deleted the code and never committed it. That was a deliberate decision: not every piece of working code deserves a long maintenance tail. Knowing when not to productionize something is as important as knowing how to do it.
Production Code and Deliberate Decomposition
The most interesting use case — and the one where Claude Code adds the most long-term value — is in writing production-ready code.
Here, the interaction is far more constrained. I don’t ask Claude Code to “build a whole module” or solve vaguely defined problems. Instead, I break work down into precise, well-scoped tasks where I already have a clear solution strategy in mind. Claude Code then accelerates execution: generating implementation details, test scaffolding, or boilerplate far faster than I could manually.
This workflow has sharpened my awareness of a critical distinction between junior and senior contributors — whether human or AI. Neither Claude Code nor a junior developer can meaningfully solve a request like:
Make the data pipeline more reliable.
The problem is too vague and admits too many interpretations. What does work is a precise problem statement rooted in system understanding, for example:
We need to make the data pipeline between X and Y more robust when incoming data violates quality constraints. The pipeline should attempt batch ingestion first, fall back to row-by-row processing on failure, emit warnings and alerts for invalid rows, and continue ingesting valid data.
This level of clarity doesn’t come from writing code — it comes from understanding the system, its failure modes, and its business constraints. Claude Code is highly effective once that understanding is in place.
Mental Models as the Real Senior Skill
Many experienced software engineers have written about LLM-assisted coding and reached a similar conclusion: senior developers are not defined by how elegant their syntax is, but by their ability to reason about systems before typing a single line of code.
Using Claude Code has made this insight tangible for me. It didn’t just help me solve concrete problems faster — it surfaced a skill I had developed over years without fully naming it: the ability to form accurate mental models of complex systems and to translate them into actionable, precise instructions.
That skill turns out to be equally important when working with AI tools, mentoring junior colleagues, or designing resilient data systems. Claude Code doesn’t replace it; it amplifies it. And in doing so, it has become one of the most effective mirrors of my own strengths as a data scientist working in production-heavy, cross-disciplinary environments.