Almost every company wants to do more with data and AI. Far fewer have the people to do it. Experienced data and machine learning professionals are in high demand, and competing for them on salary alone is a hard game to win. The good news is that you don't need a large team on day one. You need the right roles, in the right order.
The most common mistake: starting with data scientists
Many teams start by hiring a data scientist and asking them to "do AI". Then the data scientist discovers the data is scattered across systems, inconsistent and hard to reach. They end up spending most of their time on plumbing, and the models never make it out of a notebook.
Models are only as good as the data underneath them. Build the foundation first.
A sensible order of roles
- Data engineer. Builds the pipelines that bring data together reliably. This is usually the first and most important hire.
- Analytics engineer or BI developer. Turns raw data into clean, trusted models and dashboards that answer real business questions. This is where value usually shows first.
- Data analyst. Works with the business to ask the right questions and interpret the answers.
- Data scientist or ML engineer. Builds predictive models and AI features, once the data is ready to support them.
- MLOps or platform engineer. Makes models reliable in production, with monitoring, retraining and deployment pipelines. This becomes essential once more than a model or two is live.
Alongside these, someone needs to own the "why": a product owner or business lead who decides which problems are worth solving and measures whether the work pays off.
Hire the core, borrow the specialists
Not every skill needs a permanent hire. A useful rule of thumb:
- Hire the roles you'll need every day for years. These are usually the data engineering and analytics core, who build up knowledge of your business and your data.
- Borrow the roles needed for a phase or in short bursts: an architect to design the platform, an ML specialist for a first use case, an MLOps engineer to set up production practices and then hand them over.
Borrowed specialists work best when they're embedded in your team rather than working at a distance, and when handing over knowledge is part of the job from the start.
Look for the right profile, not a unicorn
Job descriptions that ask for one person to cover engineering, statistics, cloud, deep learning and stakeholder management rarely get filled. Be specific about the one or two skills the role must have, and accept that people will grow into the rest. Strong engineers from adjacent fields, such as backend developers who know SQL well, often make excellent data engineers.
A small team with clean data and a clear question will beat a large team without either.
Start small, show value, then grow
Pick one business problem where better data would clearly help, such as a forecast, a report everyone argues about or a manual process. Build the smallest team that can solve it end to end, show the result, and grow from there. A visible early win makes the case for the next hire far more easily than a strategy deck.
How we help
We help teams find data engineers, analytics engineers, data scientists and ML engineers from the market, and embed them in your team, whether you need a single specialist for one phase or the core of a new data function.
Building a Data & AI team? Tell us what you're trying to achieve, and we'll help you work out which roles to fill first.