ML / Data Practitioner
The deeper data-science and machine-learning route — the longest path here, with the highest ceiling. For people who like maths, data and patience, and want to build models, not just use them.
Typical pay: €50k–€90k+ (IE) · £42k–£80k+ (UK) estimate — verify before relying on it
The short version
ML / Data Practitioner is the deep end — the route into building and training models rather than using them. It's the longest path on this site, and the most credential-sensitive, but it has the highest ceiling. If you genuinely like maths and data and you're playing a long game, this is your path.
A day in the life
You're cleaning and exploring a messy dataset, then framing the actual problem to solve. You train and compare models, and — more importantly — you figure out whether they're really any good, not just whether the accuracy number looks nice. You write up what you found clearly enough that others trust it. It's careful, methodical, deeply satisfying work for the right person.
Who it suits
Be honest with yourself here: this suits people who enjoy maths, statistics and data, and who are patient. If that's not you, one of the other AI paths will get you working sooner and happier. If it is you, the ceiling is high and the work is genuinely interesting.
Is this you?
Find your path → — and if you scored lower on coding or maths comfort, we'll likely point you to a faster route first. Or talk to a mentor → for a straight answer on whether this is for you.
An honest note. This is the longest path here — realistically a year or more from a standing start. IE/UK salary and demand figures are estimates, not guarantees. We'd rather tell you that up front.
Your roadmap
Foundations
Build the data and programming base.
- Learn Python properly
- Learn statistics and probability
- Get fluent with pandas and SQL for data wrangling
Core skills
Learn machine learning for real.
- Classic ML with scikit-learn
- Model evaluation done properly (not just accuracy)
- Deep-learning basics
- Pick a specialism — NLP, computer vision or tabular
Portfolio
Show end-to-end, credible work.
- Build 2–3 end-to-end projects with clean writeups
- Show results (Kaggle, benchmarks, or real outcomes)
Job-ready
Target junior DS / ML roles.
- Target junior data scientist or ML engineer roles
- Consider a formal cert or conversion course as a credibility signal
- Be patient — this path is more credential-sensitive than app dev
What you'll need to start
- Comfort with maths and statistics
- Programming ability (or commitment to learn)
- Patience — this is the long path
Related paths
AI Application Developer
Build real software on top of LLM APIs — chatbots, RAG assistants, tool-using agents. The most in-demand technical entry into AI right now, and one where a strong portfolio often beats a formal ML background.
AI Ops / MLOps
Deploy, monitor and keep AI systems reliable in production. If you've got an infrastructure or DevOps background, this is one of the fastest-growing and most natural ways to move into AI.