Machine Learning Engineer vs Data Scientist: Key Differences and Which to Hire First

Most comparisons of machine learning engineers and data scientists are written for people choosing a career. This one is for the person signing the offer letter. If your company has data, a product and a vague plan to “do something with AI,” you’ve probably asked which of the two to bring on first. The short version: data scientists figure out what the data can tell you, and machine learning engineers turn that into software that runs inside your product. Which one you need first depends on where you are today, not on which title sounds more advanced.

Below you’ll find a side-by-side comparison, what each role does day to day, how their skills and pay differ, and a five-question decider that recommends where to start. If you already know you need someone to put models into production, you can hire ML engineers from Latin America through LatamCent.

The Short Answer

A data scientist works on questions. A machine learning engineer works on systems. The data scientist explores your data, runs experiments and builds a first model to prove an idea. The ML engineer takes a proven model and makes it reliable, fast and cheap enough to run for every user, every day. On a mature team they sit next to each other and hand work back and forth.

What a Machine Learning Engineer Does

An ML engineer is a software engineer who specializes in models. They turn a prototype into a service your product can call. That means rewriting research code so it runs reliably, building training pipelines that retrain on fresh data, serving predictions at the speed your users expect, and watching for the slow slide in accuracy that happens when real-world data drifts away from the training set.

A typical week might include packaging a recommendation model behind an API, making it fast enough that pages don’t stall while it thinks, setting up alerts for when prediction quality drops, and working with the backend team on how the product should behave when the model is unsure. They care about latency, cost per prediction, test coverage and on-call runbooks as much as they care about accuracy.

The role has grown fast because the gap between “we have a model” and “customers use the model” turned out to be wide. On many teams, getting a model into production takes more engineering than building it did.

What a Data Scientist Does

A data scientist turns raw data into answers. The Bureau of Labor Statistics describes the job as using analytical tools and techniques to extract meaningful insights from data, and that’s a fair summary. In a SaaS company, that often means working out why customers churn, which features drive upgrades, whether a pricing test worked, or how many support tickets to plan for next quarter.

Their week looks different from an ML engineer’s. They spend more time in SQL and notebooks, less in production code. They design A/B tests, build forecasting and churn models, present findings to product and leadership, and decide which questions are worth a model at all. A good data scientist will sometimes tell you that the model you wanted isn’t needed and a well-built dashboard will do. When you hire a data scientist, look for that judgment as much as for technical depth.

“We had large client contracts on the line, and LatamCent helped us deliver quality work without stretching our team too thin.”

Skills and Tools Compared

Both roles write Python and both understand machine learning. The difference is where each one goes deep.

Programming and Software Engineering

This is the widest gap. ML engineers write production code: modular, tested, version-controlled and reviewed. They know data structures, APIs, concurrency and how to profile slow code. Data scientists write code to answer a question, and a lot of it lives in notebooks that nobody else runs. Many data scientists are strong programmers, but it’s rarely what they’re hired for. If a candidate’s code samples are all notebooks, you’re looking at a data scientist profile.

Statistics and Modeling

Here the gap runs the other way. Data scientists go deeper on experiment design, hypothesis testing, causal inference, regression and choosing the right metric. They’re the ones who notice that your A/B test didn’t run long enough or that a spike in sign-ups came from a single campaign. ML engineers know modeling well, especially deep learning and the practical side of training, but they tend to treat statistics as a tool rather than the main craft.

Production, MLOps and Infrastructure

This belongs to the ML engineer. Containers, orchestration, CI/CD for models, feature stores, model registries, monitoring and retraining schedules are all part of the job. It’s also the skill set that’s hardest to hire for, which is why the ML engineer interview puts so much weight on system design and production stories. Most data scientists have deployed something at some point. Few have kept a model healthy in production for a year.

Salary and Cost Compared

In the US, the two roles are paid close to each other at the same level. 2026 salary surveys put mid-level ML engineers at $149,000 to $192,000 and mid-level data scientists at $138,000 to $175,000. At senior level the ranges are $168,000 to $221,000 and $157,000 to $194,000. The Bureau of Labor Statistics puts the median data scientist salary at $120,230, a figure that covers all levels and industries, and projects 35% job growth over the next decade.

Job posting averages show a wider gap (about $186,000 for ML engineers against $131,000 for data scientists), likely because ML engineer postings lean senior and cluster in high-cost cities. At the same level, expect a difference of around 10%.

US figures from 2026 salary surveys. LatAm figures are what US companies pay remote engineers and data scientists in Latin America. Salaries only, before benefits or fees.

Hiring in Latin America changes the math for both roles. Remote ML engineers working for US companies earn about $64,000 at mid level, and the ML engineers LatamCent places average about $54,000. Salary data shows a wider gap between the two roles in Latin America than in the US, so treat the data scientist figures as a floor: senior data scientists with strong ML or production skills are paid much closer to ML engineer rates. For budgets, benefits and recruiting fees, see our full ML engineer cost breakdown.

Where the Two Roles Overlap

The line between the roles is blurry, and titles don’t help. Some companies call their ML engineers “data scientists, machine learning.” Others post “ML engineer” roles that are mostly analytics. Read the responsibilities, not the title.

The shared ground is real: both roles clean data, engineer features, train and evaluate models, and explain results to non-technical people. Both need to understand your business well enough to pick the right problem. And both work with a third role you shouldn’t forget, the data engineer, who builds the pipelines that feed everyone else.

You’ll also see “AI engineer” in job posts. It usually means an engineer who builds products on top of large language models and third-party model APIs: prompts, retrieval, evaluation and guardrails. It overlaps heavily with ML engineering on the production side and needs less classic model training. If your roadmap is mostly LLM features, an ML engineer with that experience is often the right fit. Our AI and machine learning hiring page covers the other specialist roles we hire for.

Which One Should You Hire First?

Start from the problem you have today. The table below is based on the situations we run into most often when hiring for SaaS teams, and the decider after it gives you a recommendation in about a minute.

Whichever way the decider points, write down what the person should deliver in their first 90 days before you write the job post. It’s the fastest way to find out whether you’re describing a data scientist, an ML engineer or two different jobs squeezed into one.

Can One Person Do Both?

Sometimes, and at an early-stage company it’s often the smart move. A full-stack ML engineer can explore data, build a model and deploy it well enough for a first use case. For a startup with one ML feature and no data team, one strong generalist beats two specialists you can’t keep busy.

The setup breaks down as the work grows. Once you have several models in production, a real experimentation program and leadership asking for analysis every week, one person ends up doing two jobs at half depth. The usual pattern is to start with a generalist, then add a specialist for whichever side becomes the bottleneck: a data scientist for the analysis side, or a second ML engineer for production. If you’re at that point, it’s worth talking to vetted machine learning engineers who can cover both sides while the team grows.

What to Do Next

Run the decider above with the person who owns your data roadmap, and write down the 90-day outcomes for the role it points to. If it’s an ML engineer, our ML engineer interview questions will help you build the loop. If you’re still unsure after that, tell us what you’re building and we’ll tell you which profile we’d look for.

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