AI Engineer vs. Data Scientist: Key Differences and Career Paths

Both AI engineers and data scientists play important roles in today’s tech-driven world. While some overlap exists between the two positions, their jobs are not identical. Data scientists are focused on interpreting data and drawing actionable conclusions, while AI engineers prioritize building machines that can perform various tasks without constant human involvement.

In this article, we’ll break down the unique job responsibilities and career paths of data scientists and artificial intelligence (AI) or machine learning (ML) engineers in a way that helps you understand which option may be the best fit for you. We’ll also cover what role you may need to hire for if you’re an employer looking to fill a gap within your team or organization.

What is an AI engineer?

The main responsibilities of an AI engineer include programming and training the various networks and algorithms that make up an AI system. They regularly implement machine learning techniques such as deep learning and natural language processing to develop high-quality applications. Their goal is to develop AI solutions with predictive models capable of performing a wide variety of tasks, from robotics to computer vision applications.

While artificial intelligence will never replace human wisdom or decision-making, AI engineers can build machine learning models that help businesses and organizations enhance efficiency, trim costs, and make well-informed business decisions. As they explore problems in computing, AI engineers may perform research to develop theories or produce machine learning algorithms and frameworks that can ultimately solve these problems.

What is a data scientist?

Data scientists are skilled at reading and interpreting raw data and developing real-world conclusions or applications from their understanding. They know the basics of data analytics, like how to clean and analyze unstructured data to help teams and organizations make data-driven decisions or produce meaningful insights. They comprehend the unique complications of working with big data, and they understand how to perform data cleaning and data management even when working with large amounts of data.

A data scientist can also identify patterns in large datasets and make sense of the data through various techniques and analytical skills. These skills enable data scientists to share their findings with clients, colleagues, and other stakeholders as they recommend various decisions or suggest improvements. Through data mining, data scientists can also optimize business processes using predictive analytics and future forecasting.

Skill set and technical know-how

AI engineers and data scientists must possess a wide range of knowledge and various technical skills. Let’s get more specific about what you’ll need to know and do in order to succeed in each role.

AI engineers

AI engineers should be well-versed in each of the following areas:

• Programming languages. Artificial intelligence engineers should be familiar with common programming languages such as Python, Java, and C++.
• Advanced math. A thorough understanding of linear algebra, calculus, and statistics is quite helpful in this role. Awareness of derivatives and integrals will also help when working with concepts like gradient descent.
• Signal processing. An ability to solve problems with signal processing is essential for feature extraction, which plays a key role in machine learning. These abilities include experience working with signal processing algorithms like Wavelets, Bandlets, and Curvelets.
• Neural network architectures. AI engineers often face issues with translation, speech recognition, and image classification. Each problem can be countered by a deep understanding of neural networks and the roles that they play in productive AI systems.
• Problem-solving skills. As an AI engineer, you should have the ability to identify and develop informed decisions for various problems based on your research and expertise.
• Communication skills. A big part of your job as an AI or machine learning engineer will involve sharing your findings with different individuals, including people who lack significant experience in tech.
• Attention to detail. Since a small programming issue can impact the entire system, AI engineers should have a sharp eye for detail and an ability to resolve any errors that arise while testing systems.

In addition, AI and machine learning engineers will benefit from learning how to use tools such as Hive, SparkAI, Apache Hadoop, and TensorFlow to build models and improve business operations. They should also understand how to incorporate APIs that enable apps to communicate with each other in real time.

Data scientists

Data scientists should also be accustomed to working with programming languages like Python. In addition, they should have experience in the following areas:

• Statistics and probability. Data scientists will use statistical analysis to organize and comprehend data, making concepts like Bayesian statistics, descriptive and inferential statistics, hypothesis testing, and regression analysis especially important here.
• Database management. To keep data organized and easy to access, data scientists should have experience with data collection and storage in systems like Altair, Talend, or Alteryx Designer Cloud, as well as SQL knowledge.
• Machine learning. Machine learning is the science of training computers on how to become programmed over time without direct human involvement. It involves finding patterns in data from inputs or input and output combinations.
• Generative AI. Expertise is needed to leverage generative AI techniques to create synthetic data, enhance predictive models, and address gaps in datasets, enabling more robust data analysis and machine learning applications.
• Data visualization. When sharing data with stakeholders, data scientists will need to know how to visually represent data through data modeling that is clear, compelling, and easy to understand. Visualization tools like Microsoft Excel, Tableau, and PyTorch will be incredibly valuable when performing statistical modeling.
• Cloud computing. Platforms like Amazon Web Services (AWS) and Google Cloud allow data scientists to store and access data within the cloud.

Salary

Both AI engineering and data science jobs can be lucrative roles with high potential earnings. In the next section, we’ll take a deeper look at how salaries can vary between these two positions.

AI engineers

In the U.S., the median salary for a computer and information research scientist, a category that includes AI engineers, is just over $145,000, according to the Bureau of Labor Statistics (BLS). The top 10% often make in excess of $233,000, while the bottom 10% may fall to just above $82,000 annually. Colleges and universities are likely to pay at the lower end of the scale, while software publishers and research and development in science and engineering typically offer higher average salaries.

In a city like San Francisco, a lead AI engineer often makes well over $219,000. The average salary is roughly $194,000 in a city like Washington, DC, but still on the higher end of the range. As you get into midsize cities like Little Rock, Arkansas, the typical pay for a lead AI engineer starts to dip closer to $158,000. Remote opportunities often give AI engineers the option of living somewhere affordable while working for a top-end organization in a large city.

As demand for artificial intelligence engineers increases, it’s possible that average salaries will rise as well. The field of computer and information research science as a whole is projected to grow by 26% through the year 2033, according to the BLS. This demand could result in around 3,400 new openings each year, many of which will focus on AI models and applications.

On Upwork, experienced freelance AI engineers often charge between $75 and $100 per hour, depending on project complexity and skill set. Those with specialized expertise in deep learning, natural language processing, or robotics may command even higher rates.

Data scientists

Entry-level data scientists make around $79,500 per year in the U.S. on average. The lowest 10% of data scientists earn less than $64,000 per year, while the top 10% exceed $92,000. The salary of a data scientist in San Francisco would exceed $99,300 annually. At the same time, a professional working in a similar role in a smaller city like Oklahoma City might come in below $73,000 each year. Progression to a top data scientist position can mean a salary from $143,000 to $210,000. Like AI engineers, data scientists often have the option of remote work, so they can live where they want and look for jobs or projects in the highest-paying markets.

The need for skilled data scientists is forecast to grow by 36% through the year 2033, meaning it’s a role in high demand. The Bureau of Labor Statistics expects that more than 20,800 new job openings will need to be filled each year through 2033. 

Freelance data scientists typically earn $35 to $250 per hour, with rates varying based on experience, domain expertise, and familiarity with platforms like AWS, Tableau, or Python-based ML frameworks like Scikit-learn and TensorFlow.

Education

A first step in considering either of these roles is to determine what education or qualifications you’ll need in order to pursue a job in the field. Along with a formal education, staying up to date on advancements in the field is a critical component of both of these roles.

We’ll provide a detailed explanation in the sections below.

AI engineers

AI engineers must have significant expertise or experience in software development, programming, and data engineering. A bachelor’s degree in computer science or a related field is a great starting point and could be the primary educational requirement for certain positions.

However, many jobs will prioritize candidates who have a master’s degree or a specialized certification in a field like machine learning. If you’re thinking about pursuing a job in AI engineering, you may consider working toward a master’s in computer science or information systems.

Data scientists

Typically, data scientists will hold a bachelor’s degree in mathematics, statistics, or computer science. A degree in a related field may also be helpful if it includes training in algorithms, statistical techniques, and high-level statistics.

Specific positions may prefer a candidate with a master’s or even a Ph.D. Additional certifications in statistical analysis, cloud computing, and machine learning can help you further stand out as you apply for jobs in this field.

In addition, if you’re looking at a specific industry or field, relevant experience could be important. For example, if you’re pursuing work in an asset management company, you may have a better chance of garnering consideration if you can include any experience you have working in investments or banking on your resume.

Career paths

While a relevant educational background will help you get a starting position as an AI engineer or data scientist, you’ll define your career path as you continue to build expertise and take on an area of specialization. Both of these positions apply to various industries. Let’s dig deeper and consider how you may move up the ranks in either field from an entry-level role to a more senior position.

AI engineers

As we mentioned in the section on education, you’ll have the easiest time finding work as an AI or machine learning engineer if you have a relevant bachelor’s degree. Some jobs won’t require a master’s degree, but it certainly won’t hurt your chances. Regardless, if you’re starting your career with little to no experience, you’ll likely begin working in an internship or entry-level position.

Some startups may be more likely to hire professionals who are newer to the workforce; this could be an option for you to consider as you weigh different choices. It’s common for startups to have highly focused niches with specific AI-related needs. If you’re new in the field but have some experience or knowledge in the area of need, you may be an important asset to the company since they won’t have to pay for an expert at the top of the pay scale. You can learn and grow with the company.

The key decision you’ll make as you pursue your career path is to determine which type of artificial intelligence you want to specialize in. While the starting point for many AI jobs is similar, choosing to focus on natural language processing, machine learning, computer vision, or robotics can rapidly lead to quite different and exciting applications. In addition, as industries are seeing transformative change with AI, you may take on a strong supporting role in specialized businesses such as banking, health care, or many other industries.

If you want to pursue a managerial path, after several years on the job, you may have opportunities to supervise other technical professionals within the AI technology fields or across a broader computer and information systems workforce.

Data scientists

Like AI engineers, data scientists will probably need a bachelor’s degree in a relevant field to get their foot in the door. It’s common for professionals in this sector to start their career in an entry-level role with the title of a junior data scientist. In this role, you’ll work on the fundamentals of data analysis, such as predictive analysis, extracting and cleaning data, or filling other roles as assigned by a senior data scientist.

As you move forward in your career, you may become a mid-level data scientist after your first few years on the job. In this role, you’ll enjoy more autonomy in your work, but you’ll also be expected to bring more skills and knowledge to the table than an entry-level data scientist. You may also work with senior data scientists on higher-leverage projects more frequently than before.

Senior data scientists usually have three to seven years of experience. They incorporate more advanced tools into their work than junior or mid-level data scientists, and they’re responsible for monitoring all the organization’s methodologies based on the specific parameters agreed upon by stakeholders. They may also have responsibilities related to mentoring or managing junior data scientists.

There’s still one level higher than a senior data scientist. As a data science manager, you’ll oversee the big picture. You’ll be the one responsible for choosing key performance indicators (KPIs) for your team, setting strategy and future goals, and hiring team members to help make the organization’s vision a reality. To qualify for a position like this, you’ll probably need several years of on-the-job experience and at least one year of supervisory experience.

How generative AI is reshaping career paths in data science and AI engineering

The rise of generative AI is changing—not replacing—both data scientist and AI engineer roles. As tools like ChatGPT, GitHub Copilot, and generative model APIs become more widespread, professionals in these fields are expected to evolve their workflows, not abandon them.

For AI engineers, generative AI accelerates the development of ML models, allowing faster experimentation and automation of repetitive coding tasks. Rather than writing every algorithm from scratch, engineers can use AI-generated code as a starting point—freeing up time to focus on system design, performance optimization, and ethical implementation.

For data scientists, generative AI enables quicker data cleaning, automated report writing, and dynamic querying across large datasets. It supports faster insight generation, but doesn’t replace the nuanced statistical analysis or business understanding that drives real value.

Ultimately, the most in-demand professionals will be those who blend technical fundamentals—like Python, SQL, and statistical modeling—with the ability to guide generative AI tools toward strategic outcomes. Whether you’re hiring or building your skill set, understanding how to work with AI is becoming essential.

Common freelance projects for AI engineers and data scientists

Whether you're looking to hire or take on freelance work, platforms like Upwork offer a wide range of opportunities in both AI engineering and data science. These roles often overlap, but the types of projects you’ll find tend to reflect their unique focus areas.

Freelance projects for AI engineers may include:

• Training machine learning models. Building and fine-tuning ML models using Python, TensorFlow, or PyTorch to automate real-world tasks like customer segmentation or fraud detection.
• Developing AI-powered applications. Projects involving computer vision, natural language processing (NLP), or recommendation engines, especially in industries like health care or finance.
• Customizing AI APIs. Integrating machine learning frameworks or large language models into existing systems via custom APIs.
• Improving predictive models. Enhancing model accuracy and performance using advanced techniques like deep learning and neural networks.

Freelance projects for data scientists may include:

• Data analysis and visualization. Analyzing raw data to extract actionable insights and presenting findings using tools like Tableau, Excel, and SQL.
• Building dashboards and reporting pipelines. Helping stakeholders track KPIs through interactive dashboards and cloud-based solutions like AWS or Spark.
• Exploratory data analysis (EDA). Preparing large datasets for model training or stakeholder review, including data cleaning and feature engineering.
• Developing predictive analytics. Applying statistical analysis and machine learning techniques to forecast outcomes or optimize business processes.

Freelancers with certifications in tools like scikit-learn, Hadoop, or AWS often stand out in competitive project bids, especially when demonstrating strong communication and decision-making skills alongside technical expertise.

How AI engineers and data scientists collaborate on real-world projects

While data scientists and AI engineers bring different technical skills to the table, their collaboration is essential to delivering effective AI solutions. In many real-world scenarios, these roles work side by side to turn raw data into intelligent systems.

A typical workflow might start with a data scientist performing exploratory data analysis, managing large datasets, and building predictive models using machine learning algorithms. Once insights are extracted and datasets are prepared, the AI engineer steps in to scale the solution—deploying ML models into production environments, integrating APIs, and ensuring system performance across applications.

For example, in a health care project, a data scientist might use SQL and Python to identify disease risk patterns, while the AI engineer builds a deep learning model to detect those risks in real time from diagnostic images. This synergy between data analytics and software development makes AI implementation more robust and actionable.

Freelance teams on Upwork often combine these roles, offering end-to-end support for everything from model training and data cleaning to full-scale deployment using tools like TensorFlow, Spark, and AWS.

Find AI engineers and data scientists on Upwork

We’ve covered the key differences between working as an AI engineer and a data scientist. You’ve learned that artificial intelligence engineers build algorithms and training networks. In contrast,  data engineers spend more time cleaning and extracting data to prepare insights to share with stakeholders. If you still aren’t sure which is the best option for you, set aside time to research each role further and identify which option might best suit your skills and interests.

If you’re ready to start looking for your next position, you can use Upwork to find artificial intelligence and machine learning jobs and data science jobs. Upwork makes it easy for talented professionals to connect with individuals and organizations hiring for full-time and freelance positions.

You can also use Upwork if you’re looking for an AI engineer or a data scientist to solve a key business problem or join your team long term. Post your job today and get ready to receive proposals from qualified professionals who bring impressive skills and experience to the table.