Stop Getting Ghosted for Senior Data Roles. Your Git Skills May Be to Blame*.

You’re an experienced data scientist. You can build models in your sleep, your SQL is sharp, and you know your way around the cloud. So why are you getting passed over for the high-impact, high-paying roles you know you’re qualified for?

Let’s cut the crap. It’s probably not your modelling skills.

The brutal truth is that in today's market, "good enough" Git skills are a silent career killer. Hiring managers for senior roles don't just want a data scientist. They want someone who can work seamlessly with an engineering-focused team, manage complex projects without making a mess, and demonstrate a level of professionalism that junior candidates simply don't have.

Your messy commit history and clumsy workflow? It could tell them you’re not ready for the big leagues.

If you’re serious about upgrading your career and not just getting another mediocre job, you need to treat Git like the first-class skill it is. This isn't about memorising a hundred commands. It's about knowing which ones matter and using them to prove you're a senior-level professional.

Just this week, I was teaching a group of experienced data scientists from the US and the UK how to use Git to be more productive and collaborate better. The same issues came up again and again. They were brilliant analysts, but their collaboration workflows were holding them back. Getting this right is what separates the players from the pros.

Here’s the no-BS checklist of the Git skills that will actually get you hired for the job you deserve.

The Bare Minimum (If you don't know these, don't even apply)

You're experienced, so git commit, git push, and git pull should be muscle memory. But are you really using them effectively?

• git clone: You know how to copy a repo. Great. Are you comfortable cloning, setting your upstream, and immediately creating a new branch to work from? That's the pro move.
• git branch: You use branches. But do you have a consistent, clean naming convention? Or is your repo a graveyard of branches named test, wip, and final_final?
• git switch: Simple, but powerful. You should be able to instantly switch between your feature branch, the main branch, and a colleague's branch to review their work.
• git merge: The default. You know how to merge. But do you know when not to? More on that below.
• Meaningful Commit Messages: This isn't a Git command, but it’s a non-negotiable skill. If your commit messages are "updated code" or "fixed bug," you look like an amateur. Use the feat:, fix:, refactor: convention. Be concise, but clear. Explain the why, not just the what.

The OK, You're a Professional Checklist

This is where you separate yourself from the junior crowd. These commands show you think about collaboration and code quality.

• git rebase -i (Interactive Rebase): This is your secret weapon. Instead of a messy merge commit, you create a clean, linear history. It shows you respect your colleagues' time and the project's integrity. Use it to squash your "work-in-progress" commits into a single, coherent commit before making a pull request.
• git stash: You're in the middle of a complex feature, and a critical bug needs fixing now. git stash lets you shelve your current work, switch branches to fix the bug, and then come back to your work exactly as you left it. It’s clean and efficient.
• git cherry-pick: You've made a brilliant fix on your feature branch that's also needed on the main branch immediately. You don't need to merge the whole feature. Just cherry-pick that one specific commit. It's surgical and smart.
• .gitignore: If you're committing .csv files, .ipynb_checkpoints, or environment files (.env), you're doing it wrong. A well-maintained .gitignore file is the hallmark of a clean, professional project. It shows you understand what belongs in a repo and what doesn't.

The You're Hired Advanced Workflows

This is what gets you the six-figure-plus job. It's not just about commands; it's about understanding and articulating your workflow strategy.

• Handling Large Files with git lfs (Large File Storage): Data science isn't just code. It's models, datasets, and artefacts. You absolutely must know how to handle large files without blowing up the repository. If you can't explain git lfs, you're not ready for a serious data science role.
• Experiment Tracking with Branches/Tags: How do you manage multiple model experiments? The best data scientists use a structured approach. They create a branch for each experiment (exp/new-feature-engineering) and use git tag (v1.2-baseline-model) to mark important commits, like the one that produced the model that went to production.
• Understanding Workflows (GitFlow vs. Trunk-Based): You don't need to be a zealot for one over the other. But you do need to be able to discuss the pros and cons. Can you explain why a long-lived develop branch might be good for a project with a fixed release schedule, but trunk-based development is better for teams practising continuous deployment? This shows strategic thinking, not just technical execution.
• git bisect: Your model's accuracy suddenly dropped, and you have hundreds of commits since the last good run. git bisect is the magic command that automates a binary search through your commit history to find the exact commit that introduced the bug. Knowing this command will make you a hero.

Stop Playing Small. Start Acting Like a Senior.

Mastering this checklist isn't about being a "Git expert." It's about demonstrating that you're a high-calibre professional who can be trusted with complex, mission-critical projects. It's the unspoken language of the best engineering and data teams.

Stop telling yourself you can get by with the basics. The market is too competitive for that. The best roles demand the best practices.

If you're tired of being stuck and ready to land a job that truly reflects your experience and ambition, then it's time to get serious. These are the skills that will get you there.