# Data Scientist vs. Machine Learning Engineer: Key Differences and Career Insights Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/blog/data-scientist-vs-machine-learning-engineer Source observed: 2026-11-05T00:30:55.200Z Last checked: 2026-11-05T00:30:55.2+00:00 Indexed: 2026-11-06T06:47:05.709+00:00 --- # Data Scientist vs. Machine Learning Engineer: Key Differences and Career Insights **Machine Learning** · Jordyn Haime · Last updated a month ago ![Data Scientist and Machine Learning Engineer at a computer](https://storage.ghost.io/c/a5/f5/a5f5c2f0-4c62-43c7-891b-662a0b6d68b3/content/images/2024/09/Data-Scientist-Machine-Learning-Engineer-at-Computer.jpeg) We compare data science and machine learning engineering roles, including responsibilities, education, skills, example job descriptions, career paths, salaries, and interview-preparation resources. ## Key takeaways - Data scientists analyze data to uncover actionable insights, solve complex business problems, and predict trends using statistical and machine-learning models. - Machine learning engineers build, productionize, and automate predictive models. - When a data scientist develops a useful predictive model, a machine learning engineer may deploy it elsewhere in the business or make it more efficient. - Data scientists typically have STEM backgrounds and advanced degrees; machine learning engineers generally have more experience with engineering tools and frameworks. - Salaries are comparable, while demand for machine learning engineers is expected to grow as AI becomes more prevalent. This article includes excerpts from our [data science](https://www.aced.io/courses/data-science?ref=exponent-blog.ghost.io) and [machine learning](https://www.aced.io/courses/ml-engineer?ref=exponent-blog.ghost.io) interview-preparation courses. The referenced examples include [determining experiment sample size](https://www.aced.io/courses/data-science/statistics-experimentation-questions/determine-sample-size?ref=exponent-blog.ghost.io), [understanding a p-value](https://www.aced.io/courses/data-science/statistics-experimentation-questions/what-is-p-value?ref=exponent-blog.ghost.io), and [predicting results from a fair coin flip](https://www.aced.io/courses/python-coding-data-scientist-questions/predict-coin-flip?ref=exponent-blog.ghost.io). ## Data scientist vs. machine learning engineer Both roles work with data and can overlap. Data scientists use broad analytical methods, while specialized data engineering, machine learning engineering, and analytics roles have grown as businesses rely more heavily on data. In the past, data scientists often handled more of the engineering needed to deploy algorithms; that work has increasingly shifted to engineers on data science teams. Smaller companies may still ask data scientists to create ML models, while larger organizations often have dedicated teams. - **Data scientists:** Interpret data using predictive modeling and machine learning; analyze, test, clean, and optimize data to solve complex problems. - **Machine learning engineers:** Develop ML models and algorithms based on data insights. ## What does a data scientist do? Responsibilities vary by company size and needs. Meta is described as generating **4 petabytes of data daily**; its data science teams can organize and analyze that information to understand user behavior and identify actions that address user needs. Business strategy teams, product managers, and engineers can use those insights to create solutions. The 4-petabyte figure is attributed to the linked [Medium source](https://medium.com/@srank2000/how-facebook-handles-the-4-petabyte-of-data-generated-per-day-ab86877956f4?ref=exponent-blog.ghost.io). ### Key responsibilities of data scientists - Collect and manage data from multiple sources. - Perform advanced data analysis. - Develop algorithms and [models](https://www.aced.io/courses/ml-engineer/ml-system-design/mlsd-model-selection?ref=exponent-blog.ghost.io) to predict future outcomes. - Create visualizations to communicate findings. - Source, process, and model data for analysis. - Collaborate across teams to turn insights into business operations. These collaborations can help businesses anticipate market dynamics, optimize operations, and improve customer experiences. Data scientists need technical expertise and the ability to explain insights in plain language. ## What does a machine learning engineer do? Machine learning engineers generally take prepared algorithms or predictive models and [deploy them](https://www.aced.io/courses/ml-engineer/ml-system-design/mlsd-model-deployment?ref=exponent-blog.ghost.io) to production. They may improve travel-time predictions in navigation apps or artist recommendations in music apps. ### Key responsibilities of machine learning engineers - Collect and maintain clean datasets. - Collaborate with data scientists to convert predictive models into automated software. - Research, design, and build ML products for deployment. - Test and experiment with models to identify and fix bugs. - Improve models through statistical analysis. - Collaborate on algorithms that address business challenges. - Deploy ML software to improve business operations. - Maintain, analyze, and refine existing algorithms. - Document ML processes. - Maintain high-quality datasets through data-processing pipelines. ML engineers focus on software engineering and [system design](https://www.aced.io/courses/ml-engineer/ml-system-design/mlsd-framework?ref=exponent-blog.ghost.io) to execute automated models effectively, rather than understanding every model in the depth expected of a data scientist. ## Education - **Data scientists:** Usually need an advanced degree such as a master’s or PhD in data science, computer science, mathematics, or statistics. - **Machine learning engineers:** Generally need at least a bachelor’s degree in computer science or a related field such as statistics, software engineering, mathematics, or information technology. Many pursue advanced degrees in engineering, data science, or computer science. ## Essential skills and tools ### Data scientists Data scientists need data analytics, business acumen, communication skills, and proficiency in techniques such as: - Data classification - Neural networks - [Regression](https://www.aced.io/courses/statistics-experimentation-questions/answer-probability-regression-questions?ref=exponent-blog.ghost.io) - Decision trees - Clustering Common languages include [Python](https://www.aced.io/blog/top-python-data-science-interview-questions?ref=exponent-blog.ghost.io), R, SPSS, and [SQL](https://www.aced.io/blog/top-sql-data-science-interview-questions?ref=exponent-blog.ghost.io). Additional knowledge includes [Apache Hadoop](https://hadoop.apache.org/?ref=exponent-blog.ghost.io), [Spark](https://spark.apache.org/?ref=exponent-blog.ghost.io), data modeling, algorithm creation, and product metrics. ### Machine learning engineers Machine learning engineers need programming, ML system design, data structures, data modeling, [ML frameworks](https://www.aced.io/courses/ml-engineer/ml-system-design/mlsd-model-selection?ref=exponent-blog.ghost.io), and conceptual ML knowledge. Python plus C++, Java, or Scala are useful, alongside mathematics and statistics. Relevant tools include: - Deep-learning frameworks: TensorFlow and PyTorch - Cloud platforms: AWS and Azure - Model-serving tools: TensorRT and TorchServe - Deployment systems: Kubernetes and Docker ## Sample job descriptions The following are **sample job descriptions** for Spotify positions. ### Sample data scientist listing Spotify’s Experience Mission team listing describes responsibilities including improving playback performance across audio and video formats; creating, validating, and monitoring video-performance metrics for non-mobile clients; identifying and prioritizing user problems; designing and analyzing A/B tests; conducting large-scale analyses; developing dashboards and data pipelines; communicating insights; and collaborating with product, engineering, and design teams. ### Sample machine learning engineer listing Spotify’s senior ML engineer listing describes designing, building, evaluating, shipping, and refining Spotify Search products; collaborating across user research, design, data science, product management, and engineering; prototyping and productionizing solutions for Spotify’s hundreds of millions of active users; and analyzing experiments affecting users and key metrics. ## How to get a job ### Data science path 1. Choose a STEM major such as computer science or mathematics. 2. Consider an advanced statistics or computer-science degree. 3. Learn Python, SQL, and R. 4. Learn machine learning, [deep learning](https://www.aced.io/blog/top-deep-learning-interview-questions?ref=exponent-blog.ghost.io), and data visualization. 5. [Build a portfolio](https://www.aced.io/courses/data-science/data-communications-questions/past-project-example-1?ref=exponent-blog.ghost.io), contribute to open source, and refine your GitHub pages. 6. Gain practical experience through internships involving real-world data-science problems. ### Machine learning engineering path 1. Earn a bachelor’s degree in computer science, statistics, software engineering, mathematics, or information technology. 2. Consider an advanced engineering, data-science, or computer-science degree. 3. Consider certification through [Google](https://cloud.google.com/learn/certification/machine-learning-engineer?ref=exponent-blog.ghost.io), [Amazon](https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/?ref=exponent-blog.ghost.io), or a university. 4. Learn Python, C++, deep-learning concepts, cloud platforms, model-serving tools, and deployment systems. 5. Gain entry-level experience in data science or software engineering. ## Strengthening your application The article recommends highlighting projects on a GitHub profile or blog, including data cleaning, exploratory analysis on a [Kaggle dataset](https://www.kaggle.com/competitions?ref=exponent-blog.ghost.io), and ML projects. Keep code visible and documented with a README explaining setup and project purpose. Communication matters in both roles, so practice presenting insights to nontechnical audiences through storytelling and visualization. See our [data science resume guide](https://www.aced.io/blog/data-science-resume?ref=exponent-blog.ghost.io). ## Salary comparison Salaries vary by role, seniority, and location. The article gives these figures: | Role | Stated range or figure | |---|---| | Data scientist | Around **$90,000** at entry level; up to **$300,000 or more annually** at senior levels; median annual wage **$165,000** in the United States | | Machine learning engineer | Base salary around **$98,000 per year** at entry level; up to **$210,000 annually**; average approximately **$165,000 per year** | The article says median salaries are similar and that data scientists may earn more at senior levels because of advanced technical skills and task complexity. It links to [Levels.fyi](https://levels.fyi/?ref=exponent-blog.ghost.io) for data-professional salaries. ## Career paths ### Data scientists Candidates often begin through self-teaching, online courses, and personal projects, then enter as research assistants or junior data scientists. With experience, they may specialize in big data, ML, or AI, advance to senior positions, lead complex projects, manage teams, and mentor others. ### Machine learning engineers Many begin with portfolio projects, freelance work, internships, or entry-level data-science and analytics roles, often starting in software engineering. This early experience builds the skills and tools needed for ML engineering. ## FAQs ### What is the difference? Data scientists extract insights and make business recommendations; machine learning engineers implement ML algorithms to collect data more efficiently and accurately. ### What skills do data scientists need? They need predictive analysis, machine learning, data visualization, Python and R, strong communication skills, and typically an advanced degree. ### What skills do machine learning engineers need? They need mathematics and statistics, Python and C++, ML frameworks, and cloud-computing knowledge. ## Interview preparation and coaching - [Browse machine learning engineer interview questions](https://www.aced.io/questions?role=ml-engineer&src=blog) and answers from real candidates. - [Book time with a Machine Learning Engineer coach](https://www.aced.io/coaching?role=ml-engineer). Coaching options include mock interviews, career coaching, and resume review. [Browse coaches](https://www.aced.io/coaching?role=ml-engineer&src=blog). - Create a [free account](https://www.aced.io/signup?src=blog) for interview guides, tips, and courses. ## Related courses ![Machine Learning Engineer Interview Prep Course](https://images.ctfassets.net/x78yjrjc11pq/6UfkHxUyLQVfDHSfLNQtWa/1be1539a65b4f6f15201f5b573c0a578/ML_Engineer_Interview_Prep_Course.jpeg?w=800&h=800) ### [Machine Learning Engineer Interview Prep Course](https://www.aced.io/courses/ml-engineer) **6 courses · 9,400 students.** We prepare candidates for machine learning roles at Meta, Google, Amazon, Apple, Microsoft, Nvidia, and other companies through mock interviews, frameworks, senior-candidate advice, ML system design, core concepts, coding, and behavioral interviews. ![Behavioral Interviews: A Practical Guide for Engineers](https://images.ctfassets.net/x78yjrjc11pq/47qcoHjzFZXE37GoacedkB/9d8e3f4910d8b06b21e76745cd9cbff3/AdobeStock_1142146335.jpeg?w=800&h=800) ### [Behavioral Interviews: A Practical Guide for Engineers](https://www.aced.io/courses/eng-behavioral) **28 lessons.** We help engineers prepare for senior+ behavioral interviews at FAANG and Tier-1 companies, using guidance from staff and principal engineers. ## Related blog posts - [Top 39 Deep Learning Interview Questions (2026 Guide)](https://www.aced.io/blog/top-deep-learning-interview-questions) — a month ago · 20 min read - [Top AI/ML Companies Hiring Now](https://www.aced.io/blog/best-ai-ml-companies-hiring-2024) — a month ago · 3 min read - [Top 20 Python ML Interview Questions and Answers (2026 Guide)](https://www.aced.io/blog/top-python-machine-learning-interview-questions) — a month ago · 13 min read - [Machine Learning Interview Questions & Answers (2026)](https://www.aced.io/blog/top-machine-learning-interview-questions) — a month ago · 15 min read --- [Read this page’s summary](https://www.aced.io/agents/api/public/v1/page-tldr?url=https%3A%2F%2Fwww.aced.io%2Fblog%2Fdata-scientist-vs-machine-learning-engineer&trace=t_679ff9ab98f3) ## Ask a follow-up about this page Need a more specific answer? 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