ML Engineer vs. Data Scientist: Differences & Earning Potentials

As businesses increasingly rely on data-driven strategies, the demand for professionals who can use the power of data has surged.

While sharing some similarities, machine learning (ML) engineers and data scientists have distinct roles and skill sets. ML engineers are specialists in deploying machine learning models, while data scientists possess a broader skill set encompassing data collection and interpretation.

Misconceptions often blur the lines between these roles. Some assume that data scientists solely focus on data analytics, while others believe ML engineers only build models. However, these professionals often collaborate closely, with data scientists laying the groundwork for analysis and ML engineers refining models for deployment.

In this article, we’ll explore the differences between ML engineers and data scientists, uncover the unique skills each role demands, and explore the earning potentials associated with these critical positions in the tech industry.

Defining roles and responsibilities

The different roles of ML engineers and data scientists share some responsibilities and skill sets.

Both ML engineers and data scientists require a solid foundation in programming languages such as Python and Java to effectively manipulate and analyze data. Additionally, they must be well-acquainted with key machine learning frameworks to efficiently build and deploy ML models.

However, some responsibilities are unique to each position, which are detailed in the sections below.

Data scientist roles and responsibilities

Data scientists use the power of data to drive insights and inform business decisions. Their role mainly involves collecting and processing relevant data using AI techniques. For example, in health care, a data scientist might gather patient records from different sources and clean and standardize them for analysis.

Data scientists also assess which machine learning algorithms and statistical techniques are most suitable to solve specific problems identified in the data.

Responsibilities of a data scientist include:

• Data collection and cleaning. Data scientists are responsible for collecting relevant data from various sources, cleaning it by preprocessing (handling missing values and outliers), removing duplicates, and ensuring data quality and integrity.
• Algorithm selection. Data scientists identify patterns in the data to determine which machine learning algorithms and data structures are most appropriate to solve specific business problems.
• Custom model development. While they often use pre-trained models and existing frameworks, data scientists also tailor these models to address unique business challenges.
• Experimentation. Data scientists design and conduct A/B tests and experiments to evaluate the effectiveness of new features or product enhancements.
• Communication. Data scientists translate complex data findings into actionable insights, communicating their recommendations to stakeholders. They guide strategic decisions based on data-driven insights.

Machine learning engineer roles and responsibilities

The primary responsibility of ML engineers is scaling AI models to production, which entails rigorous testing, fine-tuning, and deploying models for user interaction.

For example, this might involve fine-tuning a manufacturing plant’s predictive maintenance model. With predictive analytics, a plant can maximize its ROI on capital investments by integrating a maintenance schedule that keeps machines operating at peak performance without wasting time or resources.

Key responsibilities of an ML engineer include:

• Model optimization. ML engineers focus on refining and enhancing machine learning models to improve their performance for various applications.
• Software development and software engineering skills. ML engineers are also software developers and should be able to write large-scale code to integrate models into existing systems or data pipelines.
• Engineering scalability. Ensuring that machine learning systems can handle big data and user interactions, and optimizing them for high availability and reliability, is a core function for ML engineers.
• Continuous testing. After a system is deployed, ML engineers conduct frequent, rigorous testing and validation of models to ensure their accuracy and robustness in real-world scenarios.
• Collaboration. ML engineers need to be able to collaborate with cross-functional teams, including data scientists, DevOps team members, and product managers, to align ML solutions with business objectives.

How is each role important to businesses?

Data scientists and ML engineers work closely with organizations to understand their mission and optimize operational processes. However, data scientists spend most of their time working with raw data and fundamental frameworks, while ML engineers refine machine learning systems and expand their deployment.

Here’s what these roles entail and why they are critical to business success.

Data scientist

Data scientists collect business data, such as sales figures, customer demographics, or website traffic, to identify critical patterns and issues. They uncover hidden trends and anomalies through data analysis techniques like regression analysis and statistical methods like probability modeling.  

Data scientists then use this information to anticipate future trends and behaviors, a technique known as predictive modeling. For example, in finance, they create credit risk models to aid lending decisions. These predictions enable businesses to develop strategic plans, optimize operations, and enhance customer experiences. Overall, data scientists bridge the gap between data and actionable insights, driving informed decision-making across industries.

ML engineer

ML engineers are instrumental in optimizing machine learning frameworks for real-world applications. For instance, they may fine-tune recommendation systems for e-commerce platforms, ensuring personalized user experiences by analyzing user behavior data. Unlike data scientists, ML engineers prioritize translating insights into tangible computational processes that directly impact businesses.

Overall, ML engineers tailor machine learning solutions to meet the specific needs of end users. They ensure models align with business requirements while focusing on production scalability. Like data scientists, ML engineers aim to understand business needs and ensure the solutions they develop are theoretically sound and practically impactful.

Tools and technologies

While there are differences in the tools used by data scientists and ML engineers, there are also common tools they both use. Some examples of their tech stacks include the following exclusive and shared tools.

Data scientist

Data scientists rely on a specific set of tools and technologies to extract, analyze, and visualize data models, including:

• Programming languages like R and SQL. They use R for data mining and statistical modeling, while SQL is crucial for managing databases.
• Microsoft Excel. Data scientists often use Excel for initial data exploration, basic analytics, and data cleaning tasks.
• Jupyter. Jupyter notebooks are popular for interactive data analysis, allowing data scientists to document and share their work.
• Scikit-learn. This Python library is essential for building and implementing machine learning models, offering a wide range of algorithms and tools.
• Matplotlib. Data scientists use Matplotlib to create informative visualizations and charts to help communicate insights derived from data.

ML engineer

ML engineers also rely on specialized tools and technologies tailored to their role, including:

• Programming language C++. ML engineers use C++ for optimizing and implementing high-performance machine learning algorithms and models.
• Linux. Linux is a preferred operating system for ML engineering due to its stability, customizability, and compatibility with various development tools and libraries.
• PyTorch and TensorFlow. These deep learning frameworks are used for building, training, and deploying complex neural networks.
• Kubernetes. Kubernetes is a container platform that manages and scales machine learning applications.
• TorchServe. TorchServe is a model-serving framework designed for PyTorch models, allowing ML engineers to deploy and manage machine learning models in production environments efficiently.

Both roles

Both data scientists and ML engineers share a common toolkit. Here's an overview of their shared skills and technologies:

• Python. Both roles heavily rely on the programming language Python for its versatility and extensive libraries. Data scientists use it for data analysis, while ML engineers use it for building machine learning models. In a social media model, data scientists could use Python to perform sentiment analysis on user-generated content, while ML engineers may use Python to perform advanced tasks like language translation or content recommendations.
• Cloud systems like AWS and Azure. Cloud platforms are essential for scalable and accessible data storage, processing, and model deployment. Both data scientists and ML engineers leverage platforms like AWS and Azure to access computing resources and manage data efficiently.
• Git. Version control tools like Git are crucial for tracking changes in code and data-related projects.
• Algorithms and neural networks. Algorithms are fundamental for optimizing code and data processing, while knowledge of neural networks is central to building advanced machine learning models.

Earning potentials

Both data scientists and ML engineers are high-earning roles due to their specialized skill sets and strong demand in industries including tech, finance, and health care. The following information outlines the earning potential associated with each role.

Data scientist

Data scientists make an average of $103,500 per year. This number fluctuates depending on a variety of factors like experience, location, and industry. For example, data scientists with several years of experience working in technology hubs like Silicon Valley could command higher salaries, while those in emerging markets or industries may earn somewhat less.

ML engineer

The role of an ML engineer falls under the category of computer and information research scientists, who have an average salary of $136,620 per year. However, since machine learning is a specialized role, ML engineers may earn more if they possess extensive experience, hold advanced degrees, or work for top-tier tech companies.

Career paths

The career paths for both data scientists and ML engineers involve formal education, online training, and real-world experience. However, they differ in terms of the specific skills they’ll need to acquire. The following is an overview of the typical career path for each role.

Data scientist

Below is a detailed breakdown of the typical career path for a data scientist.

• Formal education. Consider pursuing a bachelor's degree in computer science, statistics, mathematics, or a related discipline. This education provides you with a solid foundation in subjects like calculus, linear algebra, and statistics.
• Programming proficiency. Expand your skill set by becoming proficient in programming languages like Python, R, and SQL. Online courses, tutorials, and coding boot camps can help you develop these essential skills.
• Data visualization skills. Learn data visualization techniques through online platforms like Coursera to communicate your findings. Tools like Matplotlib and Tableau create compelling visual representations of data.
• Gain practical experience. You can start your career journey with entry-level positions or internships. Or, you can offer your services for small projects on platforms like Upwork. Look for opportunities to work on projects that involve data collection, analysis, an reporting. As you grow your reputation, you can take on bigger projects.
• Build a portfolio. As you progress, build a portfolio of personal projects demonstrating your ability to clean, analyze, and visualize data. Showcase your portfolio to potential employers to highlight your practical skills.
• Advancing to senior roles. With experience under your belt, aim for senior positions where you may lead teams, collaborate on strategic initiatives, and use data-driven insights to shape company strategies and decision-making.

If you aspire to transition into an ML engineer role, consider further education through online courses or on-the-job training, especially in deep learning, machine learning frameworks like TensorFlow and PyTorch, and neural network architectures. This transition can open doors to roles focused on designing, implementing, and optimizing machine learning systems and algorithms, taking your career in an exciting new direction.

ML engineer

Outlined below is a typical career path for ML engineers.

• Formal education. Begin with a strong educational foundation, typically a bachelor's or master's degree in computer science, machine learning, artificial intelligence, or a related field. This education provides a deep understanding of the theoretical underpinnings of machine learning and computer science.
• Machine learning expertise. Develop expertise in machine learning algorithms, techniques, and frameworks through online courses or certifications like IBM’s Machine Learning Certificate to understand areas such as supervised learning, reinforcement learning, and deep learning.
• Software tools. Gain proficiency in software tools and technologies like Jupyter for model development and Kubernetes for scaling. You can do so by following online tutorials provided by these platforms.
• On-the-job training. Gain practical experience through on-the-job training or internships. You can also look for projects on work marketplaces like Upwork. Work on projects involving model development, optimization, and deployment to understand real-world challenges.
• Build a strong portfolio. Develop a portfolio of projects demonstrating your ability to design, build, and optimize machine learning systems. Include details about the challenges you've tackled, the models you've developed, and the impact your work has had on real-world applications. A strong portfolio highlights your expertise and provides tangible evidence of your contributions to potential collaborators.

ML engineers can transition into the role of a data scientist, but it requires expanding their skill set and focusing on additional areas. These ML engineers should enhance their expertise in data preprocessing, statistical analysis, and visualization through online courses, certifications, or on-the-job training to make this transition successful.

Job outlook

Below, we'll explore the prospects for ML engineers and data scientists to shed light on the expected demand for these professionals in the years ahead.

Data scientist

In our increasingly data-driven world, companies rely on data scientists to analyze large amounts of data, including user engagement statistics, patient records, logistics data sets, and autonomous vehicle information.

For example, as self-driving cars become more prevalent, data scientists will play a crucial role in analyzing and interpreting vast amounts of data generated by these vehicles to ensure safety, efficiency, and reliability.

With an anticipated 35% growth in demand over the next decade, data scientists will continue to be at the forefront of driving innovation, making data-driven decisions, and addressing complex challenges across various industries.

ML engineer  

Machine learning engineers are in high demand as machine learning continues to shape industries and drive innovation. Machine learning applications span various sectors, including health care, transportation, and finance.

For instance, in autonomous vehicles, ML engineers develop algorithms for navigation and collision avoidance. In natural language processing, they enhance chatbots and language translation services.

The projected growth for computer and information research scientists, which include ML engineers, is strong, with a projected 23% increase in demand by 2032. This rapid growth reflects ML engineers' critical role in AI model development and scaling, helping to ensure they will continue to be sought-after professionals in the future.

Find ML engineer and data scientist jobs on Upwork

ML engineers and data scientists are critical players in today’s data-driven landscape but differ in their primary focus and skill sets. ML engineers specialize in optimizing and deploying machine learning models, making them experts in AI implementation. On the other hand, data scientists have a broader role encompassing data collection, cleaning, and analysis, making them adept at uncovering insights from data.

To determine the best fit for your interests and strengths, explore both fields and consider where your passion lies—whether it's in crafting advanced machine learning solutions or delving into the intricacies of data analysis.

If you're looking for opportunities in ML engineering or data science, Upwork offers a wide range of job listings in both areas. Explore the platform to find positions that align with your expertise and career aspirations.

For businesses seeking to hire ML engineers or data scientists, Upwork is a valuable resource to connect with skilled professionals. Post your job listings on Upwork to discover some of the world’s leading talent ready to contribute to your data initiatives and fuel your business growth.