What Recruiters Really Look For, and What Admissions Algorithms Get Wrong

Everyone tells you to learn Python and statistics. Useful, yes, but that is not what gets you hired. Tech recruiters look for proof that you can solve a real problem, with real users, inside a team and a deadline. University admissions platforms often reward very different signals. Between these two logics there is a gap. You can close it if you show the right evidence.

Recruiters read for outcomes. A small tool that someone outside your coursework actually used says more than five polished notebooks. A short note that a dashboard reduced manual work or that a simple rules model cut drop-off beats a vague claim that you “built an XGBoost.” They pay attention to how you frame a problem, what constraint you were working under, what success metric you chose, and why you picked a baseline over a heavy model. They also scan for adult habits: clean data, reproducible runs, a requirements file, a few tests, basic logging.

Communication matters just as much. A one-page write-up in plain language, a readable README, a two-minute demo that a non-technical stakeholder would trust, all of this travels well. Initiative travels too. If you found the data, negotiated access, fixed edge cases, wrote the docs, and closed the loop with a user, titles matter less.

Admissions algorithms and large intake processes often reward something else. They lean on static thresholds like GPA and test scores. They collapse uncertainty into brand signals, so a school name acts as a proxy for ability. They favor keyword matches, which pushes applicants to write CVs that please a machine but hide real outcomes. Format landmines are common: the wrong file type or a mislabeled field can eject a good candidate. These systems are also mostly blind to context. Care work, migration, language learning, or a switch from another field are hard to explain inside a rigid form. Complexity is sometimes overvalued, while clarity and maintainability are treated as secondary.

So what should you do. Build one small project that touches a user and finish it in a month. Scope the question, set a simple baseline, measure a result, write a short postmortem about what worked and what you would do next. If you cannot measure impact, collect a short quote from a user who tried the tool. Make the work reproducible with a requirements file, a seed, a couple of tests, and clear notes on data. Curate your GitHub so that three pinned projects tell three different strengths: a clean pipeline, a simple model that shipped, and a tiny API or utility. Practice a two-minute demo that explains the problem, the approach, the result, and the lesson without jargon. When you write a statement for a degree, start with a real problem you solved and map each course you want to take to a gap you still have.

If you are coming from another field or another language, say it plainly and make it a strength. For example: you moved from language education to applied analytics to work on access and inclusion, and your domain knowledge helped you design a better solution for real users. One specific project that shows this crossover will speak louder than a long list of certificates.

Recruiting is noisy. Admissions is rigid. Neither can read your mind. Reduce the guesswork. Put forward clear, checkable signals that you can frame a problem, build something reliable, explain it, and learn fast. Industry talks and schools can be helpful as background, but the center of your story is the work you ship and the skill you plan to scale next.

About the author

Lilian Lori (Liliane Laurie) is an independent education consultant and multilingual writer. She covers access to higher education in Europe, focusing on digital admissions, language policy, and international mobility. Her current projects include WindowToFrance and STUDYON. Follow her on Telegram @lilian_lori.