Data Scientist vs. AI Engineer — Choosing the Right Career

data scientist vs ai engineer

The field of computer science is continuously expanding, and among the many professions within it, data scientist and artificial intelligence (AI) engineer are two critical roles. Both professions hold immense significance in the tech world and are essential to the development and implementation of advanced technology.

While data scientists use their analytical expertise to transform data into actionable insights, AI engineers focus on developing intelligent machines that can perform tasks without human intervention.

As the global AI market is projected to reach over $202 billion by 2026, and the data science market estimated to exceed $178 billion by 2025, it’s evident that there is a growing demand for professionals in these areas. In fact, the data scientist profession is expected to grow by 36% by 2031, while the AI engineering profession — along with other occupations within the computer and information research science field — is expected to grow by 21% by 2031 — both much faster than most other professions.

Various industries are now actively seeking to hire data scientists and AI engineers, including top sectors such as:

  • Finance and insurance
  • Healthcare
  • Retail
  • Manufacturing
  • Telecommunications
  • Automotive
  • Digital marketing
  • Media and entertainment
  • Education

Considering a career in one of these innovative fields? Read on to explore the differences between data scientists and AI engineers and determine which profession is right for you.

Data Scientist Overview

AI Engineer Overview

Educational Requirements

Roles & Responsibilities

Earning Potential

Technical Skills

Which Should You Choose?

Data Scientist Overview

Data scientists are highly skilled professionals who use various technologies and techniques to analyze and extract valuable insights from large data sets. They possess a unique combination of skills that includes:

  • Algorithm development
  • Mathematics
  • Statistics
  • Design
  • Engineering
  • Communication
  • Management

To analyze and extract insights from data, data scientists utilize technologies such as big data analytics, cloud computing and machine learning. These technologies enable them to work with large amounts of data, extract patterns and predict future outcomes.

Data scientists employ the following tools and techniques to help businesses identify patterns, trends and relationships in data:

  • Statistical methodologies
  • Distributed architecture
  • Visualization tools
  • Data-oriented technologies, such as Hadoop, Spark, Python, SQL and R

The insights that data scientists provide can be used to analyze user metrics, forecast business risks, evaluate market trends and make more informed decisions to reach organizational goals. With their skill set and expertise, data scientists play a critical role in helping organizations remain competitive and achieve their objectives.

AI Engineer Overview

Also highly skilled, AI engineers specialize in building and deploying intelligent autonomous models using various machine learning techniques such as neural networks, deep learning and natural language processing (NLP). They use the following techniques to create AI-based applications:

  • Language translation
  • Visual identification
  • Contextual advertising based on sentiment analysis

One of the primary roles of an AI engineer is to collaborate with business stakeholders to build AI solutions that can optimize operations, service delivery and product creation. They work closely with various departments within an organization to identify areas where AI solutions can be implemented to improve efficiency, productivity and profitability.

AI engineers are responsible for the end-to-end development of AI solutions. This includes designing, building, testing and deploying AI models into applications. To accomplish this, AI engineers use numerous tools and technologies, including:

  • Machine learning
  • Algorithms
  • Software engineering principles
  • Neural networks
  • Computer vision

A career in AI engineering is considered future-proof because it’s a critical part of many frontline innovations and technological advancements. As organizations continue to invest in AI technology to boost operations and cultivate new products and services, the demand for skilled AI engineers is only expected to grow.

Educational Requirements

Both data scientists and AI engineers require a strong educational foundation in mathematics, statistics and computer science. However, there are some differences in the specific educational requirement for each profession.

A data scientist position requires:

  • A graduate degree in computer science, economics, social sciences, physical sciences or statistics
  • A strong foundation in math and statistics
  • Strong grasp of programming in Python and R
  • Proficiencies with handling data/data management techniques

An AI engineer career requires:

  • A graduate degree in math, statistics, economics, engineering, computer science, IT, linguistics or cognitive science
  • A deep understanding of machine learning, deep learning, neural networks and natural language processing
  • Familiarity with programming languages like Python, R, Java and C++

While a master’s degree or Ph.D. is typically required to attain a position as a data scientist or an AI engineer, employers will have different requirements, including prior work experience or proficiency in certain programming languages. The University of San Diego’s online Master of Science in Applied Artificial Intelligence and Master of Science in Applied Data Science are two excellent options for students interested in pursuing a career in either of these fields.

Roles & Responsibilities

The roles and responsibilities of data scientists and AI engineers have significant overlap, as both professions involve utilizing machine learning algorithms and other technologies to extract valuable insights and create AI-powered applications. However, there are also key differences in their specific areas of focus:

For example, data scientists’ responsibilities include:

  • Identifying business problems and gathering relevant datasets for analysis
  • Cleaning, transforming and exploring data before analysis
  • Utilizing cutting-edge methods for data mining to develop new insights
  • Selecting and instituting appropriate machine learning algorithms to solve business challenges
  • Applying different statistical modeling and machine learning techniques to assess and optimize model outcomes
  • Enhancing model hyperparameters to achieve the best results
  • Identifying trends, patterns and correlations in large datasets using analytical methods and machine learning models
  • Coordinating with data analysts, AI engineers and other stakeholders to improve and inform business decision-making
  • Communicating insights and findings to business stakeholders in a clear and persuasive manner

AI engineers’ responsibilities include:

  • Developing intelligent AI algorithms and deploying them into applications
  • Building scalable, efficient APIs to integrate data products and sources into applications
  • Creating Infrastructure as Code to ensure reproducibility and scalability of AI solutions
  • Producing and sustaining architecture using leading AI frameworks
  • Utilizing version control tools like GIT and TFS for continuous integration and versioning control
  • Testing and deploying AI models into production environments
  • Developing user interfaces to display model performance and insights
  • Implementing software engineering concepts like continuous delivery, auto-scaling and application monitoring
  • Creating MVP applications that encompass the entire AI development process
  • Overseeing the AI project’s overarching needs

Overall, data scientists are responsible for collecting and analyzing data to extract insights, while AI engineers focus on using those insights to create AI-powered solutions that can improve business operations and develop new products.

Earning Potential

The amount of money you can make is an important consideration when choosing a career path. Both data science and AI engineering are lucrative fields that offer competitive salaries. Here is a breakdown of the earning potential for each profession.

Data Scientist:

  • The average annual salary is $126,136, with ranges from $78,000 to $206,000 per year, depending on factors such as years of experience, level of expertise and job location
  • Senior data scientists with more than 10 years of experience can earn more than $150,000 per year
  • The top-paying locations for data scientists in the US are the San Francisco Bay area, New York and Seattle

AI Engineer:

  • The average annual salary is $156,648, with ranges from $79,500 to $266,500 per year, depending on factors such as years of experience, level of expertise and job location
  • AI engineers with over 10 years of experience can earn more than $200,000 per year
  • The top-paying locations for AI engineers in the US are the San Francisco Bay area, New York and Washington D.C.

Technical Skills

Professionals in data science and AI engineering need various technical skills in order to excel in their roles. Below are some of the key technical skills required for each profession.

Data scientists should maintain:

  • Advanced mathematics and statistical knowledge to help identify patterns and trends in data
  • Knowledge of programming languages such as R and Python to help clean, manage and analyze data
  • Knowledge of big data tools such as Spark, Hadoop, Hive and Pig to work with large datasets
  • Understanding of data visualization tools such as QlikView and Tableau to communicate insights effectively
  • In-depth understanding of data cleaning, management and mining
  • Familiarity with databases and related languages such as SQL and other relational databases

AI Engineers should possess:

  • A solid foundation in computer science and software engineering concepts and principles
  • Strong mathematical and algorithmic skills for designing and developing AI models and algorithms
  • Proficiency in programming languages such as R and Python to develop, implement and test AI models
  • Knowledge and experience with Unix and Linux environments for development and deployment
  • Experience in data evaluation and testing to ensure the accuracy and performance of AI models
  • Proficiency in deep learning, machine learning, image processing, NLP, computer vision and neural network architectures to build AI models

Data Scientist or AI Engineer: Which Should You Choose?

Choosing between a career as a data scientist or an AI engineer is a decision that ultimately depends on your interests and skill set. Both professions offer a promising future with a strong job outlook and high salary potential. It’s important to note that advanced education, such as a master’s degree, can provide a competitive edge and increase your marketability in these fields.

If you’re interested in pursuing a career in AI or data science, the University of San Diego’s 100% online Applied Artificial Intelligence master’s program and Applied Data Science master’s program offer excellent opportunities to develop the necessary skills and knowledge you need to be successful. With the right education and training, you can position yourself to thrive in either of these increasingly important professions.

Considering a Master’s in Artificial Intelligence?

Free checklist to help you compare programs and select one that’s ideal for you.

Cover of 8 Questions to Ask Before Selecting an Artificial Intelligence Master's Degree Program Book