AI & data traineeship

Value doesn't come from building more. It comes from building the right thing: something the business truly needs, people actually use, technology can support, and where AI and data are used responsibly.

That takes understanding what the business is truly struggling with, separating hype from impact, and deciding which problem is worth solving first. That bridge between technology, business and people is where the value sits now, and no agent is taking it over.

Belgian companies are investing in AI and data, but many are still working out what to do with it. That's where you come in.

Here's how.

THE TRAINEESHIP IN SHORT

By doing the work, with the right people around you.

26 months. One contract with Ormit Talent. Two to three AI and data assignments at different client companies.

You don't just sharpen your AI and data skills. You build the business sense and people skills that make them count, with training, coaching, expert mentoring and a group of peers behind you.

Why does that matter? Most data jobs make you better at the tech and leave the rest to chance. Here, you build all three. That's why our clients hand you work they'd normally save for someone years further along.

WHAT YOU'LL ACTUALLY WORK ON

Which assignments will you get? We can't tell you yet. Each one comes with its own context, questions and level of AI and data maturity, and asks a bit more of you than the last. What stays the same is the mix: hard skills in AI and data, the business sense to know what's really needed, and the people skills to get others on board.

In practice, that mix comes down to six areas. We saw them come back assignment after assignment at our clients. You will not meet all six in one assignment, and the exact tools depend on where you land. But across your assignments, your training and your mentor, you get hands-on with several and learn about all six.

1. Problem definition. Where every good project starts: turning vague requests into clear data questions. Which decision does it support? What does success look like, and is the data even there?

2. Data analysis. The hands-on part. You clean and test datasets, find the quality issues and the anomalies, and challenge the data someone else hands you. Often in SQL and Excel, sometimes Python where the question calls for it.

3. Insights creation. Where data turns into decisions. You build dashboards in Power BI or Tableau that someone actually decides with, not reports that describe what already happened. Plus the storytelling that gets them read.

4. Smart automation. Where the manual work disappears. You spot the tasks that keep coming back and get rid of them, with Power Automate, an API integration or an AI agent where it earns its place. And you judge a tool on what it delivers, not on how new it is.

5. Responsible AI and data use. Where trust gets built. You check for bias and data quality, explain what a model does and doesn't do, know which rules you're working under, and build together on clear data governance.

6. Translation to business and IT. Where it all lands. You explain the constraint to the business and the priority to the data engineers. And you set expectations before the work starts, not when the deadline moves.

TRAINING

Your training runs on two tracks, and both count. On the technical side, you get room to go deeper: certifications, a platform subscription or dedicated time to master the tools your assignments ask for, with your technical mentor to challenge your approach. 

On the business and people side, the core trainings are the same for everyone. It starts with discovering who you are. From there, you build towards working better with others, getting to the core of what a business needs, and bringing people along. 

Then you put it to the test. In a hackathon and a real client case, you sit in the decision maker's seat and weigh opportunities, risks and competing priorities, not just the data question. Experts watch and give you feedback on your approach.

"Let's be honest: I signed for the hard skills. If the programme hadn't been technical enough, I wouldn't have signed at all. The training and coaching on human skills? A nice bonus, I thought. Looking back, that's exactly what took me to the next level. I just didn't know what I didn't know." 
Louis – Alumnus Data traineeship

COACHING & MENTORING

Most people only get a coach once they're managing a team. You get one from day one.

Every month, you sit down with your Ormit Talent coach to talk about how it's really going. They'll challenge you, give you sharp feedback and practical advice, and ask the occasional question you'd rather not answer. That's the point.

Together you work on how you show up: in a tough meeting, with a sceptical stakeholder, when a project shifts under your feet. Step by step, you widen your range, until the situations that used to feel uncomfortable become the ones you handle best.

Your coach helps you grow as a person. For the technical side, there's Jens.

As the technical mentor of every AI & Data trainee, he knows the craft inside out. He challenges your tooling and your approach, and answers the questions a coach can't.

At the start and again halfway, Jens joins you and your coach. Together you look at your growth on both the technical and the human side, and decide what to focus on next.

PEERS

You're not doing this alone. You're part of a strong community of fellow trainees, as driven as you and going through the same thing. They share what works, challenge how you think, and make coming into the Ormit Talent office worth it 😉 Your trainings happen as a group, and the connections you build there tend to outlast the traineeship itself.

This is you

• A bachelor's or master's in data science, analytics, applied statistics, applied informatics, or business economics with a data focus. Different degree? Then you bring clear experience in one of these fields.

• You've already used it in practice, in an internship, a student job or a first job.

• English, plus Dutch or French. The second of those two is a bonus. The drive to learn it is a must.

• At least two of Python, SQL and Power BI. Whether you're strongest at building models or at turning them into something a business acts on, we want to see both hands in the data.

• AI affinity. You know your way around AI beyond the chat window, use it to work smarter, and know when not to trust it.

• A driving license, and you're happy to work at client sites across Belgium.

This is not you

• You want to stay in the code and leave the client conversation to someone else.

• You like the strategy side, but you'd rather not open the dataset yourself.

• You want to specialise in one data field and stay there for the next five years.

• You need to know today which company you'll be working for in two years.