.webp)
Key Takeaways
- AI engineers build intelligent systems, while data scientists analyze data to guide decisions—knowing the difference helps you hire the right role for your needs.
- Hire a data scientist when you need to uncover insights, validate hypotheses, or make sense of large datasets.
- Hire an AI engineer when you’re ready to automate, scale, or integrate AI into your products and operations.
Most businesses aren’t wondering, “Should I hire a data scientist or an AI engineer?”
They’re wondering, “How do I make sense of all this data?”
Both roles are critical in answering that question, but in different ways.
One builds and deploys intelligent systems. The other extracts insights to guide business decisions. Knowing the difference can save you time, budget, and a whole lot of hiring headaches.
In this article, we’ll break down the key responsibilities of each role, highlight the most important differences, and help you decide who to hire based on your business goals.
What Does an AI Engineer Do?
An AI engineer builds the systems that allow machines to learn, adapt, and make decisions. Their job goes beyond writing code—they develop the models, frameworks, and infrastructure that power AI-driven tools across your business.
While a software engineer might build your platform, an AI engineer builds the intelligence behind it.
Here’s what that typically looks like:
- Designing and training machine learning models
- Developing algorithms that process large volumes of data
- Building AI applications that automate decisions or predict outcomes
- Collaborating with product, data, and engineering teams to integrate AI features into workflows or products.
AI engineers turn raw data into functioning systems that improve over time. Whether you’re automating a repetitive task or launching an intelligent product feature, they make sure your AI isn’t just theoretical but usable, scalable, and effective.
What Does a Data Scientist Do?
A data scientist turns complex data into insights that drive smarter business decisions. While they may experiment with machine learning models, they typically don’t build the production-level AI systems themselves. Instead, they uncover the patterns, trends, and opportunities that inform and guide those systems.
Think of a data scientist as a strategic advisor. They help answer key questions like: Where are we losing revenue? What’s driving churn? What’s likely to happen next quarter based on the data we have?
Their day-to-day typically involves:
- Collecting, cleaning, and analyzing large datasets
- Building statistical models and running experiments
- Creating dashboards and visualizations to communicate insights
- Working closely with stakeholders to inform product, marketing, and growth strategies
While AI engineers build the engine, data scientists help you decide where to drive. Their work is key when your business is sitting on data but hasn’t yet turned that data into action.
AI Engineer vs. Data Scientist: Key Differences
AI engineers and data scientists often work closely together, but their roles serve different business needs and require different technical skill sets. Understanding the distinction is essential to hiring the right person for the job.
Here’s how they compare.
Focus area: Building vs. analyzing
AI engineers build systems. Their focus is on designing, deploying, and maintaining machine learning models that operate in real-time and at scale. They’re concerned with how machines learn and how those learnings get integrated into your tech stack.
Data scientists analyze data. They extract meaning from complex datasets using statistics, modeling, and experimentation. Their goal is to help your team make smarter, data-driven decisions but not necessarily to automate those decisions.
Core skill sets
AI engineers typically come from software engineering or machine learning backgrounds. They write production-grade code, work with frameworks like TensorFlow or PyTorch, and optimize models for performance and scalability.
Data scientists are more likely to have experience in mathematics, statistics, and domain-specific analytics. Their toolkit includes SQL, Python, R, and tools like Pandas or Scikit-learn for data wrangling and visualization.
Output and deliverables
An AI engineer delivers live, integrated systems like recommendation engines, chatbots, fraud detection models, or image recognition tools. Their work feeds directly into your digital products or internal tools.
A data scientist delivers insight through dashboards, reports, trend analyses, and strategic forecasts. Their output is often used by leadership to inform product direction, marketing strategies, or financial planning.
Dynamics between the roles
These roles often work best together. A data scientist might identify an opportunity—say, customers dropping off after a certain step in the funnel—while an AI engineer builds a real-time model to predict and prevent it from happening again.
In short:
- AI engineers operationalize intelligence
- Data scientists uncover it
The right hire depends on what your business needs right now and how you plan to use data to drive impact.
.webp)
When Should You Hire an AI Engineer vs. a Data Scientist?
Hiring the wrong role can slow your momentum or, worse, build tools you don’t need.
Here’s how to make the right call based on where your business is today.
Hire a data scientist when…
Your business has access to a lot of data but little clarity on what to do with it. A data scientist is ideal when you’re still in the discovery phase: looking to uncover insights, test hypotheses, or find patterns that can guide decision-making.
This is especially useful if your current dashboards aren’t giving you the full picture, or if you need to predict customer behavior, track KPIs, or understand what’s actually driving performance.
If your business needs answers before it starts building tools, start with hiring a data scientist.
Hire an AI engineer when…
You already know the problem you’re solving, and you’re ready to build a solution. AI engineers are best suited for automating processes, deploying machine learning models into production, or integrating AI features, especially when the cost of hiring is balanced by long-term efficiency gains.
If you’re scaling and need intelligent automation that adapts over time (not just one-time analysis), this is where hiring an AI engineer is a smart move.
And if that’s the case, check out our guide on hiring an AI engineer.
When you might need both
Some companies reach a point where both roles are essential. For example, a data scientist might identify a powerful pattern in customer behavior, and an AI engineer can then turn that insight into a recommendation engine.
If you’re working on a project that involves both heavy data analysis and long-term system deployment, hiring both roles (or working with a cross-functional team) ensures your data journey doesn’t hit a dead end after discovery.
Still unsure where to start? Focus on your immediate pain point.
If it’s unclear what your data is saying, hire a data scientist. And if you’re ready to build a tool that acts on that data, bring in an AI engineer.
Final Thoughts
Choosing between an AI engineer and a data scientist comes down to one thing: what you need your data to do right now.
If you’re looking to uncover trends, validate ideas, or make data-driven decisions, a data scientist is the right fit. If you’re ready to build systems that act on that data—automating tasks, making predictions, or powering intelligent features—then it’s time for an AI engineer.
And if you’re realizing you might need both, but worry your budget won’t stretch that far, don’t write it off. Hiring outside the US—particularly in Latin America—can make full-time, high-level talent more accessible without compromising quality.
Learn how nearshoring can help you build a high-performing team affordably in our article “Nearshoring: The Smartest Way to Cut Costs & Scale Your Business in 2025.”
.webp)
.webp)
.webp)
.webp)
.png)
%20(1).png)
%20(1).png)