Hiring Data and Machine Learning Engineers: What to Know Before You Start

You have a data problem and no one to own it. Perhaps reports disagree with each other, perhaps a pilot model never left a laptop, or perhaps the board has asked what your AI plan is. The instinct is to open a vacancy titled "data scientist" and wait. Then the search drags, the applicants do not match what you needed, and the role you meant to fill turns out to be a different one.

This guide helps you avoid that. It explains how data engineers, data scientists and machine learning engineers differ in practice, what official European data says about the market you are hiring in, and how to run a search that starts from the problem rather than the job title. The distinctions between roles are a working hiring heuristic, not an official classification, so adapt them to your own stack.

Key Takeaways

• Decide what the work is before you choose a title, because data engineer, data scientist and machine learning engineer roles solve different problems.
• Hiring is hard across the whole ICT field in Europe, so plan for a longer search and a smaller shortlist than you would for a generalist role.
• Company size and sector shape demand: larger firms and technology sectors use AI far more than small ones, which affects who you compete with.
• Write the brief around the first six months of work, the data you already have and how success will be judged.
• Test for judgement and communication as well as technical skill, because many of these roles sit between engineers and the business.

Start With the Work, Not the Title

Job titles in this area are used loosely. One company's data scientist builds dashboards, another's trains models, and a third's mostly cleans data. If you copy a title from a competitor's advert, you inherit their definition without knowing it.

A more reliable approach is to describe the problem. What decision should be easier in six months? What data exists, and in what state? Who will use the output? Once those answers are written down, the right role usually becomes obvious, and so does the list of skills you can safely leave out.

Data Engineer: Making Data Usable

A data engineer builds and maintains the pipelines that move data from source systems into a place where it can be used. Think reliability, structure and scale: ingestion, storage, quality checks and the plumbing that analysts and models depend on.

Hire for this role first if your data is scattered across tools, if reports take days to produce, or if no one trusts the numbers. A brilliant modeller is of little use when the inputs are unreliable. Many teams find this is the real gap behind a request for a data scientist.

Data Scientist: Answering Questions

A data scientist explores data to answer a question: why customers leave, which factors drive demand, what a change is likely to do. The work mixes statistics, analysis and communication, and the output is often an insight or a recommendation rather than a running system.

Look for this profile when you have reasonably clean data and a business question that nobody has had time to investigate. Make sure someone senior will act on the findings. Otherwise the role becomes a reporting job, and good candidates leave.

Machine Learning Engineer: Putting Models Into Production

A machine learning engineer takes models into production and keeps them working: deployment, monitoring, retraining, performance and cost. The role is closer to software engineering than research, and the best candidates are comfortable with code review, testing and infrastructure.

This is the right hire when a prototype already exists, or when you know a model will sit inside a product. If you do not yet have a clear use case, hiring this role first tends to be premature.

Where the Roles Overlap

In smaller companies, one person is often asked to do two or three of these jobs. That can work for a while, but be honest about it in the advert. Candidates read an unrealistic scope as a warning sign. If you are still shaping the engineering side of the team, the guide to writing a job description for engineers covers structure and tone that apply here too.

What Official Data Says About the Market

You do not need an exact count of machine learning engineers in Europe to make good decisions, and no official source publishes one. What is available is data on ICT specialists as a group, which is the closest official picture of the pool you are drawing from.

A Growing but Narrow Talent Pool

Eurostat reports that more than 10 million people were employed as ICT specialists in the EU in 2025, about 5.0% of total employment. The same source shows that the number of ICT specialists rose 59.4% between 2015 and 2025, while total employment rose 9.8%. So the pool is growing much faster than the workforce overall, but demand from every sector is growing alongside it.

Concentration varies widely by country. In 2025 Sweden had the highest share of ICT specialists in employment at 8.9%, while Greece had the lowest at 2.5%. Eurostat also reports that women made up 19.5% of ICT specialists in the EU in 2025. If your shortlists look uniform, widening where and how you source is a practical fix, and it is also a reasonable thing to ask of any recruiting partner.

Employers expect demand for these skills to keep rising. The World Economic Forum's survey of more than 1,000 leading employers worldwide found that big data specialists and AI and machine learning specialists are among the fastest-growing roles in percentage terms over 2025 to 2030. That is a global employer survey, not a European headcount, but it points the same way as the Eurostat figures.

One caution: ICT specialists is a broad category that includes many roles beyond data and machine learning. Treat these figures as context for the market, not as a headcount for your target role.

Hard-to-Fill Vacancies Are the Norm

Eurostat's most recent published figures on recruitment come from 2023. Among EU enterprises that recruited or tried to recruit ICT specialists, 57.5% reported hard-to-fill vacancies. The picture differs by country: Germany stood at 72.41%, Czechia at 70.47% and Spain at 30.15% of those recruiting.

The UK shows a different picture on a different measure. In the CIPD's Summer 2026 survey of 2,017 UK employers, 31% still reported hard-to-fill vacancies. That figure covers all roles and all employers, not only those recruiting ICT specialists, so it is not directly comparable with Eurostat's number. It is a reminder that difficulty depends on the role you are hiring for.

A reasonable reading is that a search for a specialist in this area should be planned for difficulty, especially in Germany and Czechia. That is this guide's interpretation of the data, not a finding from Eurostat. The Spanish figure is lower, but it describes difficulty reported by firms, not the quality or availability of any particular profile, so do not treat it as a guarantee of an easy search.

Who You Are Competing With

AI adoption is rising quickly, so it is reasonable to expect more employers to want these skills. Eurostat reports that 19.95% of EU enterprises with ten or more employees used AI technologies in 2025, up 6.47 percentage points from 2024.

The gap by size is large. In 2025 AI use was 17% among small enterprises, 30.36% among medium ones and 55.03% among large ones. Sector matters too: 62.52% of enterprises in information and communication used AI, compared with 10.79% in construction. If you are a smaller company outside the technology sector, this guide's reading is that you are likely competing with large technology employers for the same people, so lean on what you can offer them: a clear problem, real ownership and direct access to decision makers.

Country differences are wide as well. Denmark led at 42.03%, while Romania was lowest at 5.21%. Mature markets may have deeper experience among candidates, while lower-adoption markets may hold strong people who have had fewer chances to ship AI work. That second point is this guide's inference, not a finding from the source.

If you are weighing which countries to search in, the guide to hiring software engineers in Europe sets out the wider engineering picture, and the guide to hiring across several countries covers the practical side of building a team across borders.

Writing a Brief That Attracts the Right People

Strong candidates in this field have choices, so a vague brief costs you the best of them first. Spend an hour on the brief before you spend a week on sourcing. The advice in how to brief a recruiter applies whether you are working with an internal team or an external partner.

Describe the First Six Months

List the two or three outcomes the person should deliver in their first six months. For a data engineer, that might be one reliable pipeline feeding the reports that matter most. For a machine learning engineer, it might be getting an existing prototype into production with monitoring. Concrete outcomes tell candidates what they would actually do, and they give your interviewers something to test against.

Be Honest About Your Data

Candidates want to know what they are walking into. Say what data you hold, where it lives and how clean it is. A frank description of a messy starting point attracts people who enjoy fixing it, and it filters out those who would be frustrated within a month. Overselling a clean data estate only delays the disappointment.

Make the Scope and Seniority Clear

State whether this is the first hire in the area or one of a team, who the person reports to, and whether they will have anyone to learn from. A first data hire at a small company needs breadth and independence. A hire into an established team can be more specialised. Mixing the two expectations in one advert confuses both groups of candidates.

Say What the Role Pays and Where It Sits

Include a salary range and a clear working arrangement. Some candidates will not respond without them, and the ones who do will arrive with realistic expectations. For roles in the EU this is also becoming a legal matter: under the EU pay transparency rules, employers will have to inform job seekers about the starting salary or pay range in the vacancy notice or ahead of the interview, and will no longer be allowed to ask about their pay history. EURES says that as of June 2026 it is mandatory across the EU to disclose salary ranges in job adverts or before the first interview. Neither page says how far each country has written the rules into national law, so check the position in every country where you advertise, and take legal advice where it matters.

Assessing Candidates Fairly and Efficiently

The aim of the process is to see how someone thinks about your problem, not to recreate an exam. Keep it short, relevant and respectful of the time of busy people.

Match the Interview to the Role

For a data engineer, discuss a pipeline that failed and how they found the cause. For a data scientist, ask how they would approach one of your real questions and how they would explain the answer to a sceptical colleague. For a machine learning engineer, ask how they would monitor a deployed model and decide when to retrain it. A set of general technical prompts is also useful, and the interview questions for engineers guide is a good starting point to adapt.

Test Communication and Judgement

Many of these roles sit between engineers and the business. Ask candidates to explain a technical trade-off to a non-technical listener, or to say what they would do when a stakeholder asks for an answer the data cannot support. Candidates who handle these well tend to be effective inside an organisation, and the skill is hard to teach later.

Keep the Process Short

Strong candidates can be on the market for a short time only. Agree the stages in advance, name who decides, and give feedback quickly. Every extra week and every extra round gives a competing employer a chance to move first.

Working With a Recruiting Partner

A specialist partner can help most when you know what you need but lack the network or the time. The questions to ask are practical: has the recruiter placed this kind of role, how do they source, and what will they tell you when the market is difficult? The guide to choosing a talent acquisition partner goes deeper on what to expect from one.

Workfully is an AI-native recruiting company. AI agents take the brief and match it to expert recruiters by specialism, seniority and market; recruiters source from their own networks; every search is visible in one place. If you are about to open a search for a data or machine learning role, book a demo with Workfully to see how a search would run.

Whoever you choose, share the same brief you wrote for yourself, including the first six months of work and the honest state of your data. A partner can only be as precise as the brief it receives.

FAQ

What is the difference between a data engineer and a data scientist?

A data engineer builds the pipelines and systems that make data reliable and available. A data scientist uses that data to answer questions and build models. If your data is unreliable, hire the engineer first. If it is in good shape and you have open questions, a data scientist may be the better first hire.

Do I need a machine learning engineer or a data scientist?

Choose a machine learning engineer when you already have a model or prototype and need it running reliably in a product. Choose a data scientist when you are still exploring what the data can tell you. Many companies need the data scientist first and the machine learning engineer later.

How hard is it to hire ICT specialists in Europe?

It is hard for many employers. In 2023, 57.5% of EU enterprises that recruited or tried to recruit ICT specialists reported hard-to-fill vacancies. That covers ICT specialists as a whole rather than data and machine learning roles specifically, so treat it as context.

How common is AI use among European companies?

It is growing but uneven. Eurostat reports that 19.95% of EU enterprises used AI technologies in 2025, with much higher use among large firms than small ones. The share also varies a great deal by country and sector.

Should I list a salary range in the advert?

Yes, in most cases. A range saves time on both sides and attracts candidates with realistic expectations. In the EU, the pay transparency rules mean employers must give job seekers the starting salary or pay range in the vacancy notice or before the interview. Check how each country where you advertise has applied them.

Sources

• ICT specialists in employment, Eurostat, published 2026, accessed 2026-11-09 (tier A).
• Use of artificial intelligence in enterprises, Eurostat, published 2025, accessed 2026-11-09 (tier A).
• ICT specialists - statistics on hard-to-fill vacancies in enterprises, Eurostat, published 2025, accessed 2026-11-09 (tier A).
• Future of Jobs Report 2025, World Economic Forum, published 2025-01, accessed 2026-11-09 (tier B).
• New EU rules on pay transparency explained, European Commission, published 2026-06-05, accessed 2026-11-09 (tier A).
• Salary ranges are changing recruitment. Is your company ready?, EURES (European Commission), published 2026-10-02, accessed 2026-11-09 (tier A).
• Employers keep hiring on hold as jobs growth flatlines, new CIPD research finds (Labour Market Outlook, Summer 2026), CIPD, published 2026-08-17, accessed 2026-11-09 (tier B).