Do You Need An ML Engineer or a Data Scientist? Find Out Here

So, your business works with data and machine learning. Maybe you’re building an AI-powered product, or perhaps it’s time to finally make sense of that overflowing database of marketing analytics. Either way, you need someone who can turn data into actionable insights. 

But who’s the right hire? A data scientist? A machine learning (ML) engineer? Maybe both? Maybe neither.

The ML engineer versus data scientist debate often leaves businesses unsure of which role they actually need. While both work with and have a deep understanding of data, their expertise serves very different business functions. Hire the wrong one, and you could end up with sophisticated models that never get deployed or a fully automated system that lacks meaningful insights.

In this guide, we’ll break down what each role does and discuss when to hire one over the other and how different industries use them effectively so you can make an informed decision for your business.

ML Engineers vs. Data Scientists: What’s the Difference?

The demand for machine learning in business operations is skyrocketing. With 92% of businesses looking to invest in AI, it’s no wonder that more and more businesses are seeking out data and machine learning specialists.

One of the first questions business owners need to ask themselves is whether they need a machine learning engineer or a data scientist.  While these roles share quite a bit of common ground, they each focus on different aspects of the data and AI workflow.

Understanding these key differences will help you make the right call for your specific business needs.

What does a machine learning engineer do?

Think of an ML engineer as the builder of AI systems. Their job is to take machine learning models and turn them into working, scalable applications that businesses can actually use.

• Develops machine learning models that can process data and make predictions.
• Works with software engineers to integrate AI into applications such as recommendation engines and chatbots.
• Focuses on scalability, automation, and performance tuning to make sure models work in real-world conditions.
• Works remotely in many cases due to the software-based nature of their job, making ML engineers a great choice for companies looking to hire globally.

What does a data scientist do?

A data scientist is more like a detective. They dig into data to find patterns, trends, and insights that help businesses make smarter decisions.

• Analyzes large datasets to uncover trends, customer behavior, or business risks.
• Uses statistical models and predictive analytics to forecast outcomes.
• Works with both structured and unstructured data, such as sales figures, customer reviews, and sensor data.

Key differences: ML engineer vs. data scientist

To understand the data science and ML engineering roles at a glance—where they overlap and where there are distinct differences— we’ve created the table below:

With this information, businesses can better determine which expert they need or whether a combination of both would be a more effective solution.

When Should You Hire a Data Scientist vs. an ML Engineer?

You know you need someone to help make sense of your data, but should you bring in a data scientist or a machine learning engineer? The answer depends on what you’re actually trying to do.

Let’s take a look at each role in more detail to understand which role is the better fit based on your business needs.

When a data scientist is the right fit

If your business is sitting on tons of raw data but doesn’t know what to do with it, you need a data scientist. They help businesses turn information into actionable insights, whether it’s predicting sales trends, understanding customer behavior, or identifying risks.

Best for: Businesses that need data-driven decision-making but aren’t ready to build full-blown artificial intelligence systems yet.

Where data scientists make an impact:

• Finance: Identifying fraud, assessing investment risks, and optimizing trading strategies.
• Ecommerce: Understanding customer buying habits and forecasting sales trends.
• Healthcare: Predicting patient outcomes and improving treatment recommendations.

Can you hire a data scientist remotely?

Absolutely. Many companies offshore data scientist roles to access highly skilled talent at a lower cost. Countries like India and the Philippines are ideal for offshore outsourcing, while Latin American (LatAm) countries have professionals who can do the same work as a local hire but remotely—often at a significant cost savings.

When an ML engineer is the right fit

If you already have structured data pipelines and want to put that data to work, whether it’s through automation, AI-powered tools, or predictive models, then you need an ML engineer. Their job is to take machine learning models and turn them into working products that integrate into your business.

Best for: Businesses that want AI-driven solutions that go beyond analysis and actually impact operations.

Where ML engineers make an impact:

• SaaS and tech: AI-powered chatbots, recommendation engines, and automated customer support.
• Manufacturing: Predictive (rather than preventive) maintenance to predict equipment failures before they happen and schedule maintenance accordingly.
• Retail: Dynamic pricing models that adjust in real time based on demand and competition.

Is offshoring ML engineering a smart move?

Since ML engineers work entirely in software, they’re one of the easiest remote hires for businesses looking to cut costs while accessing top talent. 

Since real-time collaboration is key to ensuring smooth deployment of AI projects, LatAm countries like Brazil, Argentina, and Mexico offer a compelling advantage. Not only do they have highly skilled engineers who can develop, deploy, and scale AI systems, but they can also do so cost-efficiently while working in time zones that overlap with US business hours. 

This makes communication easier and helps teams stay in sync without the delays that often come with global collaborations spanning vastly different time zones.

Business Growth Stage Hiring Guide: Which Role Do You Need?

When it comes to hiring, the right choice isn’t just about what you need—it’s also about where your business stands in its growth and how ready you are to adopt AI.

For startups and small businesses? Hire a data scientist

Startups usually have lots of raw data but no structured AI pipelines. A data scientist can help make sense of it all by analyzing customer behavior, forecasting growth, and identifying key trends. 

Without this foundation, an ML engineer wouldn’t have the structured data they need to build AI solutions.

A mid-sized & scaling company? Hire an ML engineer

As businesses grow, automation and AI-driven solutions become more valuable. An ML engineer can deploy models that improve efficiency, whether it’s personalized recommendations for users (like a SaaS platform) or automated fraud detection for fintech companies.

An enterprise-level company? You need both

Large-scale businesses handle huge amounts of data, and they need both roles to maximize its value. Data scientists analyze trends and inform business strategies, while ML engineers operationalize AI for real-time decision-making. 

For example, think of a global ecommerce platform that predicts customer trends (data science) and automates product recommendations (ML engineering).

Final Thoughts

At the end of the day, it’s all about what your business needs. Looking for insights? Hire a data scientist. Want to build AI-powered solutions? You need an ML engineer. 

The good news? Since both of these critical roles can be done remotely, you can hire top-tier talent from LatAm and beyond. This flexibility allows you to access either or both skill sets without overextending your budget, giving you the comprehensive data expertise you need to stay competitive.

If you're looking to hire an ML engineer, check out our guide to the 13 top staffing agencies that can help you find the right ML talent.