After years of hype and promise, artificial intelligence (AI) has finally arrived. Organizations of all types and sizes are racing to integrate AI into their business processes to make their operations more powerful, more efficient, and more profitable. A data scientist and machine learning engineer are two of the most exciting and cutting-edge professions in technology. While both involve realizing the promise of AI in business, choosing between becoming a machine learning engineer vs. a data scientist requires understanding how the two roles differ, and how they complement each other.

Machine learning engineers and data scientists are members of the team behind a company’s machine learning (ML) platform. Each position fulfills critical duties in the development, implementation, and maintenance of machine learning applications. 

Yet the roles, skill sets, and responsibilities of a machine learning engineer vs. data scientist differ in important ways. Understanding the differences and similarities of the two positions helps you decide which role is a better match for your career goals.

The Role of a Machine Learning Engineer vs. Data Scientist

The goal of machine learning and other AI-based activities is to create software applications that enhance our lives, whether in business settings or in our day-to-day activities outside of work. Machine learning engineers and data scientists are vital to the design and use of intelligent systems that naturally improve over time, with or without the assistance of humans.

One way to distinguish the roles of machine learning engineers and data scientists in intelligent system design is by seeing data scientists as the architects of a structure and machine learning engineers as the builders who convert blueprints and models into a functioning system. 

These are among the primary duties of data scientists in the creation of intelligent systems:

• Determine which business problems are suitable for ML solutions
• Visualize the many stages of the ML lifecycle (data gathering, data preparation, data wrangling, data analysis, modeling training, model testing, deployment)
• Design custom algorithms and data models
• Identify complementary data sets and generate the synthetic data that deep learning (DL) models require
• Determine the system’s data annotation requirements
• Maintain ongoing communication with all stakeholders
• Create custom tools for optimizing the modeling workflow

By contrast, the role of machine learning engineers emphasizes the deployment and operation of ML and DL models:

• Deploy and optimize ML and DL models in production settings
• Monitor the models’ performance to address latency, memory, throughput, and other operational parameters
• Perform inference testing on CPUs, GPUs, edge devices, and other hardware
• Maintain and debug the ML and DL models
• Manage version control for models, metadata, and experiments
• Optimize model workflows using custom tools

Data scientists are directly involved in the analysis and interpretation of the insights extracted from ML and DL models by applying statistical and mathematical techniques to identify patterns, trends, and relationships in the data. 

Machine learning engineers rely more on their background in programming and engineering to transform data science concepts into functional systems that are flexible, scalable, and transparent.

Machine Learning Engineer vs. Data Scientist: Skills, Education, and Responsibilities

There is a considerable amount of overlap in the qualifications needed for careers in machine learning engineering and data science. For example, both fields require technical acumen, analytical thinking, and problem-solving skills. They also rely on programming experience that typically includes Python and R programming, cloud systems (AWS, Microsoft Azure, and Google Cloud Platform, or GPC), and metadata storage and optimization.

Yet more important than the similarities in the education and skills of machine learning engineers and data scientists are the differences in their technical and educational backgrounds:

• Data scientists must be adept at statistics, data analytics, data visualization, written and verbal communications, and presentations.
• Machine learning engineers must possess in-depth knowledge of data structures, data modeling, software engineering, and the concepts underlying ML and DL models.

Data scientists tend to have a broader set of hard skills than machine learning engineers, including experience with statistical and mathematical software, query languages, data visualization tools, database management, Microsoft Excel, and data wrangling. 

The most important criteria for machine learning engineers include knowledge of ML frameworks and ML libraries, data structures, data modeling techniques, and software architectures. 

These are among the skills necessary for a career as a machine learning engineer:

• Linux/Unix operating systems
• Java, C, and C++ programming languages
• GPU architectures and CUDA programming
• Data modeling and evaluation
• Neural network architectures
• Natural language processing (NLP)
• Distributed computing
• Reinforcement learning
• Spark and Hadoop programming

The skill sets of data scientists encompass these areas:

• SQL and Python coding
• Database design and programming, including NoSQL and cloud databases
• Data collection and cleaning tools, including business intelligence (BI) tools
• Statistical analysis tools such as SPSS, Matlab, and SAS
• Descriptive, diagnostic, predictive, and prescriptive statistical analyses
• Linear algebra and calculus
• ML model building
• Model validation and deployment tools (SAS, Neptune, Kubeflow, and Google AI)
• API development tools such as Amazon AWS (Amazon API Gateway) and IBM Cloud (IBM API Connect)

The U.S. Bureau of Labor Statistics (BLS) points out that most data scientists possess a master’s degree or doctorate in mathematics, statistics, computer science, business, or engineering. (The BLS groups machine learning engineers under the category of data scientists.) Programming languages that are considered essential for data scientists are Python, R, SQL, Git, and GitHub. 

Machine learning engineers are expected to be proficient in Java, R, Python, and C++, as well as in using ML libraries such as Microsoft’s CNTK, Apache Spark’s MLlib, and Google’s TensorFlow. They’re also expected to have a strong understanding of web APIs and dynamic and static API libraries.

The Outlook for Machine Learning Engineers and Data Scientists

The BLS forecasts that the number of jobs available to data scientists will increase by 36% between 2021 and 2031, which is much faster than the average growth in all occupations. 

The World Economic Forum’s “The Future of Jobs Report 2023” places AI and machine learning specialists among the fastest-growing jobs, with an average annual growth of 30% through 2027. The report points out that 42% of the companies surveyed intend to prioritize training workers to apply AI and big data in the next five years.

Salary estimates for data scientists include the BLS reporting an average annual wage of $100,910 as of May 2021, and PayScale’s survey indicating data scientists’ average base salary of $99,344 in 2023, within a range of $71,000 and $138,000 per year. 

By contrast, PayScale puts the average base salary of machine learning engineers at $115,243 in a range from about $80,000 to $157,000 per year.

According to PayScale, the skills that have the greatest impact on the salaries of machine learning engineers are image processing (26% higher than the average), reinforcement learning (22% higher), DevOps (22% higher), and Scala (20% higher). 

Data scientist salaries are boosted by possessing skills in C++ programming (42% higher than the average), cybersecurity (39% higher), research analysis (26% higher), PyTorch software library (24% higher), and forecasting (22% higher).

A burgeoning field for data scientists is quantum computing – specifically quantum information science – which requires knowledge of quantum mechanics and the use of quantum algorithms in problem-solving applications. 

Similarly, machine learning engineers can expect a boost in their job prospects in coming years as a result of the advent of generative AI, which is expected to add as much as $4.4 trillion in economic value by increasing overall productivity, according to McKinsey’s “Technology Trends Outlook 2023” report.

Machine Learning Engineer and Data Scientist: On the Crest of the Next Tech Wave

AI technologies will have a tremendous impact on economies and job markets worldwide in the coming years, but as with every game-changing technology, there will be winners and losers. The Center for Economic Policy Research (CEPR) estimates that AI will increase global growth by 4% to 6% each year, compared to an average of an annual increase of 4% over the past few decades. 

The effect of AI on employment is less certain, but the World Economic Forum estimates that while AI will replace 85 million jobs around the world between 2020 and 2025, it will also create 97 million jobs, primarily in areas such as big data, machine learning, and digital marketing. As these figures indicate, demand for machine learning engineers and data scientists will likely remain strong for many years to come.

Image used under license from Shutterstock