Should You Hire a Data Scientist or an ML Engineer First?
Last updated:
The job titles overlap more than recruiters admit
Put three job adverts side by side and you will find a 'data scientist' expected to deploy Kubernetes clusters and an 'ML engineer' expected to design experiments. Titles are inconsistent across companies, which makes the first hire harder than it should be.
So ignore the titles for a moment and look at the work. There are roughly three kinds, and a small business needs them in a particular order.
Three roles, described by what they do all day
| Role | Spends most time on | Typical output | Hire when |
|---|---|---|---|
| Data engineer | Moving, cleaning and joining data from source systems | Reliable pipelines and tables | Data is scattered or untrusted |
| Data scientist | Exploring data, testing hypotheses, building first models | Analyses, prototypes, evidence | You do not yet know what is worth predicting |
| ML engineer | Putting models into production and keeping them healthy | Deployed, monitored services | You know the use case and need it live |
There is also the analytics engineer or BI developer, who often solves more of an SME's actual problems than any of the three. If your real need is trustworthy reports, start there.
When a data scientist is the right first hire
- Your data is already in a warehouse or a reasonably tidy database
- Leadership believes there is value in the data but cannot name the use case
- Decisions like pricing, marketing spend or stock levels would benefit from proper analysis before any model
- You have someone technical who can help get prototypes into systems
The risk: a data scientist without production support builds valuable prototypes that never ship. If you hire one first, budget for engineering help alongside, or accept that the first year is about evidence rather than deployed systems.
When an ML engineer is the right first hire
- The use case is clear, perhaps proven by an outside team or a proof of concept
- The model needs to run inside your product or core systems
- Reliability, latency and cost of running matter
- You are building AI features into a SaaS product rather than analysing internal data
For software companies adding AI features, this is usually the answer. Much of today's applied AI work is integration, evaluation and operations around hosted models rather than novel modelling, and that is engineering. We say more on this in hiring an AI engineer for SaaS.
The awkward truth: you may need a data engineer first
For many SMEs the honest answer is neither. Orders are in the ERP, customers are in the CRM, returns are in a spreadsheet, and nobody fully trusts any of them. A data scientist hired into that situation will spend their first six months doing data engineering, probably less well than a data engineer would, and will quite possibly leave.
A useful test: ask how long it would take to produce a single clean table of every customer, their orders and their returns for the last two years. If the answer is 'a day', you are ready for data science. If the answer is 'we would have to think about that', start with the data.
What one person costs, compared with alternatives
A single specialist hire is a bigger commitment than the salary. They need data access, tooling, a manager who understands their work and a reason to stay. A lone data scientist in a company with no technical peers is a common source of short tenures.
| Option | Best for | Watch out for |
|---|---|---|
| First in-house hire | Ongoing, core, well-understood need | Isolation, unclear direction, single point of failure |
| Outside team for a defined project | Proving the first use case | Knowledge leaving with the team |
| Fractional or part-time specialist | Direction setting and hiring help | Limited hands-on capacity |
| Outside build, then internal hire to run it | Getting value before building a team | Needs a clean handover plan |
Interview questions that reveal which one you are talking to
Because titles are unreliable, the interview has to do the sorting. These questions tend to show where a candidate's experience actually sits.
- Tell me about a model you built that did not go into production. Why not? A data scientist will usually talk about the business outcome; an ML engineer about the infrastructure.
- How did you know a live model was still performing well six months later? Vague answers suggest limited production experience.
- Describe the messiest data source you have had to work with and what you did about it. Good data engineers light up at this one.
- If I gave you our CRM export tomorrow, what would you do in the first week?
- When have you recommended not using machine learning?
The last question is the most useful. A candidate who has never advised against a model has either been very lucky or has not been close enough to the business to notice when one was not needed.
The route we see work most often
For a business without an existing data team, the pattern that works most reliably is: prove one use case with outside help, including the data pipeline, then hire the person whose job is to run and extend it. By then you know which profile you need because you have seen the work.
When SpiderHunts delivers data science or machine learning projects for companies planning to hire, we write the handover and the job description together, so the new hire inherits documented pipelines rather than a mystery. If you need someone to own technical direction more broadly, when to hire a technical lead covers that decision.
Frequently asked questions
What is the difference between a data scientist and an ML engineer?
Should a startup hire a data scientist?
Do we need a data engineer before a data scientist?
Can one person do data science and ML engineering?
Deciding who to hire for machine learning?
Tell us what you want the role to achieve in its first year. We will give you an honest view of which profile fits, even if the answer is that you should hire rather than use us.
Related services
What we build for problems like this one