Data Scientist vs. Machine Learning Engineer: Key Differences and Career Insights

Below, we explore the key differences between data science and machine learning engineer roles.

Key Takeaways

• Data scientists analyze data to uncover actionable insights for solving complex business problems and predicting future trends. They typically use statistical and machine learning models.
• Machine learning engineers build, productionize, and automate predictive machine learning models to provide new insights based on previous data.
• If a data scientist discovers a useful model for predicting future outcomes, the machine learning engineer could deploy that model to other parts of the business or in more efficient ways.
• Data scientists typically have a STEM background and advanced degrees, while machine learning engineers have more experience with engineering tools and frameworks.
• Salaries for data scientists and machine learning engineers are comparable. However, the demand for machine learning engineers is expected to grow as AI becomes more prevalent.

Data Scientist vs. Machine Learning Engineer

Data scientists and machine learning engineers work with data, and their roles often overlap.

While data scientists use a wide range of methods for data analysis, the demand for specialized roles—such as data engineers, machine learning engineers, and data analysts—has increased with the growing use of data in business decision-making.

Previously, data scientists were more involved in the engineering aspects required to deploy their algorithms.

Now, this responsibility has shifted to engineers on the data science team, including machine learning engineers.

In smaller companies, data scientists may still be responsible for creating ML models. However, larger organizations usually have a dedicated data science team.

• Data scientists: Interpret data using advanced techniques like predictive modeling and machine learning to analyze, test, clean, and optimize it for solving complex problems.
• Machine learning engineers: Focus on developing machine learning models and algorithms based on data insights.

What Does a Data Scientist Do?

Data scientist roles vary depending on a company’s size and needs.

Companies like Meta, which generate 4 petabytes of data daily, rely on their data science teams to organize and analyze this vast amount of information.

A data scientist might help Meta understand user behavior and identify actions to address user needs. Based on these insights, business strategy teams, product managers, and engineers can then create solutions.

Key Responsibilities of Data Scientists

Data scientists:

• Collect and manage data from multiple sources
• Perform advanced data analysis
• Develop algorithms and models to predict future outcomes
• Create visualizations to communicate findings

Data preparation involves sourcing, processing, and modeling data for analysis. Data scientists frequently collaborate with other teams to turn data into business operations.

These collaborations allow businesses to:

• Anticipate and respond to market dynamics
• Optimize operations
• Improve customer experiences

Data scientists need technical expertise and the ability to translate data insights into plain language.

What Does a Machine Learning Engineer Do?

Machine learning engineers begin after a data scientist has prepared machine learning algorithms or predictive models.

Machine learning engineers deploy these models to production to collect data more efficiently.

Machine learning engineers may help improve the accuracy of travel time predictions for navigation apps or enhance artist recommendations in music apps.

Key Responsibilities of Machine Learning Engineers

Machine learning engineers:

• Collect and maintain clean datasets
• Collaborate with data scientists to convert predictive models into automated software
• Research, design, and build machine learning products for deployment
• Test and experiment with machine learning models to identify and fix bugs
• Improve models through statistical analysis

While they create machine learning products based on models prepared by data scientists, ML engineers are not expected to understand every model in depth—that is the data scientist's role.

Instead, machine learning engineers focus on software engineering and system design to execute automated models effectively.

Machine learning engineers focus on:

• Collaborating with the data science team to develop new machine learning algorithms that address business challenges
• Deploying machine learning software to improve business operations
• Maintaining, analyzing, and refining existing ML algorithms
• Documenting machine learning processes
• Maintaining high-quality datasets through data processing pipelines

Education

The role of a data scientist is broader than that of a machine learning engineer, and therefore, it typically requires more education.

Data scientists usually need advanced degrees, such as a master's or PhD, in fields like data science, computer science, mathematics, or statistics. These programs provide the deep technical knowledge required for complex data analysis and machine learning tasks.

Machine learning engineers generally require at least a bachelor's degree in computer science or a related field, such as statistics, software engineering, mathematics, or information technology. Many also pursue advanced degrees in engineering, data science, or computer science.

Essential Skills and Tools

Data scientists require more skills than machine learning engineers, including data analytics, business acumen, and the ability to communicate solutions to non-technical stakeholders.

Key Skills for Data Scientists

Data scientists should be proficient in machine learning and predictive modeling techniques, such as:

• Data classification
• Neural networks
• Regression
• Decision trees
• Clustering

Familiarity with coding languages like Python, R, SPSS, and SQL is crucial, as these are commonly used for data analysis and visualization.

In addition to traditional programming and machine learning coding skills, data scientists should have a strong understanding of:

• Big data technologies like Apache Hadoop and Spark for handling and processing large datasets
• Developing data models
• Creating algorithms
• Business acumen and product metrics

Key Skills for Machine Learning Engineers

Machine learning engineers need to be proficient in programming, machine learning system design, and the following areas:

• Data structures
• Data modeling
• Common ML frameworks
• Conceptual knowledge of ML

Proficiency in Python and familiarity with programming languages like C++, Java, and Scala are essential for machine learning engineers. A strong foundation in mathematics and statistics is also crucial.

Machine learning engineers should also be familiar with the following tools:

• Deep learning frameworks like TensorFlow and PyTorch
• Cloud computing platforms like AWS and Azure
• Model serving tools like TensorRT and TorchServe
• Deployment systems like Kubernetes and Docker

Job Descriptions

Below are sample job descriptions for data scientist and machine learning engineer positions at Spotify.

Example Data Scientist Job Listing

A job posting for a data scientist with Spotify’s Experience Mission team lists the following day-to-day responsibilities:

• Contribute to improving the core and ubiquity playback performance across Spotify’s fast-evolving audio and video formats.
• Lead the creation, validation, and monitoring of video performance metrics for non-mobile clients.
• Use data science methods to independently identify and prioritize impactful user problems.
• Partner with other data scientists to design, implement, and analyze A/B tests across formats and platforms to deliver actionable insights.
• Conduct large-scale analyses, develop dashboards, and create data pipelines to empower data-driven decision-making.
• Communicate insights and recommendations to collaborators and stakeholders.
• Collaborate closely across disciplines with product, engineering, and design teams.

Example Machine Learning Engineer Job Listing

A job posting for a Senior Machine Learning Engineer with Spotify lists the following day-to-day responsibilities:

• Contribute to designing, building, evaluating, shipping, and refining Spotify Search’s products through hands-on ML development
• Collaborate with a cross-functional agile team spanning user research, design, data science, product management, and engineering to build new product features that connect artists and fans in personalized and relevant ways
• Prototype new approaches and productionize solutions at scale for Spotify’s hundreds of millions of active users
• Set up and analyze experiments that impact hundreds of millions of users and crucial metrics

How to Get a Job

If becoming a data scientist sounds like the right fit:

• Choose a STEM major in computer science, mathematics, or a related field.
• Consider pursuing an advanced degree in statistics or computer science.
• Learn Python, SQL, and R.
• Familiarize yourself with machine learning, deep learning, and data visualization.
• Build a portfolio to showcase your skills. Contribute to open-source projects and refine your GitHub pages.
• Gain practical experience by working on real-world data science problems in internships.

If machine learning engineering is more your speed:

• Get a bachelor’s degree in computer science, statistics, software engineering, mathematics, or information technology.
• Consider pursuing an advanced engineering, data science, or computer science degree.
• Enroll in a machine learning engineering certification program through Google, Amazon, or a university.
• Learn Python and C++, common deep learning concepts, cloud computing platforms, model serving tools, and deployment systems.
• Gain entry-level experience in data science or software engineering.

Strengthening Your Application

The job market becomes increasingly competitive as the demand for Big Data professionals grows.

• Highlight relevant projects on your GitHub profile or blog. Include diverse projects, such as cleaning data from data.gov, performing exploratory analysis on a Kaggle dataset, or completing a machine learning project.
• Ensure your code is visible and well-documented. Include a README file that explains the setup and summarizes the project.
• Communication is critical for both data science and machine learning engineer roles. Practice presenting insights for a non-technical audience using storytelling and data visualization methods.

Salary Comparison

Salaries for data professionals vary based on role, seniority, and location.

The median salaries for data scientists and machine learning engineers are similar. However, data scientists often earn more at senior levels due to their advanced technical skills and the complexity of their tasks.

Data Scientist Salary Range

In the United States, data scientists typically start with salaries around $90,000 and can earn up to $300,000 or more annually at senior levels.

The median annual wage for data scientists is $165,000.

Machine Learning Engineer Salary Range

Machine learning engineers typically start with a base salary of around $98,000 per year and can earn as much as $210,000 annually.

The average salary for a machine learning engineer is approximately $165,000 per year.

Career Paths

How do most candidates become data professionals?

Career Paths for Data Scientists

Data scientists often begin their careers through self-teaching, online courses, and personal projects to build foundational knowledge and practical experience.

They may start as research assistants or junior data scientists, gradually advancing to specialized roles in big data, machine learning, and AI.

With experience, data scientists can move into senior roles, leading complex projects, managing large teams, and mentoring junior team members. Some may also specialize in areas like big data engineering or machine learning.

Career Paths for Machine Learning Engineers

Many machine learning engineers begin by honing their skills through personal projects, which can be featured in a portfolio.

Aspiring MLEs can also seek freelance work, internships, and entry-level jobs in data science or analytics, often starting their careers in software engineering.

Early career experience in these areas helps them master the skills and tools necessary to advance to a machine learning role.

FAQs

What is the difference between a data scientist and a machine learning engineer?

Data scientists extract insights from data and make business recommendations. Machine learning engineers then implement machine learning algorithms to collect data more efficiently and accurately.

What skills do data scientists need?

Data scientists use machine learning and predictive analysis methods to address complex business problems and forecast future trends. They typically need an advanced degree, experience in predictive analysis, machine learning, data visualization, programming languages like Python and R, and strong communication skills.

What skills do machine learning engineers need?

Machine learning engineers need a strong foundation in mathematics and statistics, proficiency in coding languages like Python and C++, and familiarity with machine learning frameworks and cloud computing platforms.