Data Analyst vs. Data Scientist vs. Machine Learning Engineer

Explore the distinctions between these common job titles with the analogy of a track meet.

Author:  Nicole Janeway  |  Post Date:  Aug 2, 2020  |  Last Update:  Jun 4, 2024  |  Related Posts

This article uses the metaphor of a track team to differentiate between the role of a Data Analyst, Data Scientist, and Machine Learning Engineer. We'll start with the idea that conducting a Data Science project is similar to running a relay race. Hopefully, this analogy will help you make more informed choices around your education, job applications, and project staffing.

If you're interested in pursuing a career in one of these areas, we strongly recommend checking out our report on the best certifications spanning seven data-related domains. One of our favorites is the Certified Analytics Professional (CAP) certification, which is a great way for Data Analysts to advance their skills. (If you're just getting started, you can check out our CAP Study Groups on Facebook and LinkedIn and our study materials to help you prepare for the CAP or aCAP exam).

Contents

• Data Analyst
• Data Scientist
• Machine Learning Engineer
• ML Ops
• Conclusion
• Related Posts

🔵 Data Analyst

The Data Analyst is capable of taking data from the “starting line” (i.e., pulling data from storage), doing data cleaning and processing, and creating a final product like a dashboard or report. The Data Analyst may also be responsible for transforming data for use by a Data Scientist, a hand-off that we'll explore in a moment.

You might say that the Data Analyst is very capable of running the first part of the race, but no further.

🔴 Data Scientist

The Data Scientist has all the skills of the Data Analyst, though they might be less well-versed in dashboarding and perhaps a bit rusty at report writing. The Data Scientist can run further than the data analyst, though, in terms of their ability to apply statistical methodologies to create complex data products.

The Data Scientist is capable of racing the entire lap. That means they have the skills required to query data, explore features to assess predictive power, select an appropriate crop of models for training and testing, conduct hyperparameter tuning, and ultimately arrive at a statistics-powered model that provides business value through classification or prediction. However, if an organization loads its Data Scientist with all these responsibilities — from data ingest through data modeling — the Data Scientist won't be able to run as well as if he or she were asked to run only the second part of the race, focused on the data modeling.

Overall, the team's performance will improve if a business analyst conducts the querying and data cleaning steps, allowing the Data Scientist to focus on statistical modeling.

If you want to read more about Data Science, check out our article An Introduction to Data Science for Technology Leaders.

Grab your free copy of our report today

If you haven't already, check out our detailed writeup on the seven top certifications across a variety of data-related domains. Aim higher with your career ambitions for 2025 and pursue the proven qualifications that will help you demonstrate your value. Check out our comprehensive report to learn how to attain the credentials to break into a new field or accelerate your career trajectory.

🔶 Machine Learning Engineer

The Machine Learning Engineer could be thought of as the team's secret weapon. You might conceptualize the MLE as the person designing track shoes that empower the other runners to race at top speeds.

The Machine Learning Engineer may also be focused on bringing state-of-the-art solutions to the Data Science team. For example, an MLE may be more focused on deep learning techniques compared to a Data Scientist's classical statistical approach.

Increasingly, the distinction between these positions is blurring, as statistics becomes the domain of easy-to-implement packages in Python and R. Don't get me wrong — a fundamental understanding of statistical testing remains paramount in this career field. However, with growing frequency, the enterprise data scientist is asked to execute models powered by deep learning. This refers to the field of Data Science enabled by GPU-based computing, where typical models include neural networks like CNNs, RNNs, LSTMs, and transformers.

Machine learning researchers at companies such as Google Brain, OpenAI, and Deep Mind design new algorithmic approaches to advance toward state-of-the-art performance on specific use cases and, ultimately, the goal of building artificial general intelligence.

🚌 ML Ops

Another job title related to Data Science is MLOps. This refers to the responsibility of productionizing a model —in other words, creating a version of the model that is accessible to end users. MLOps is focused on creating a robust pipeline from data ingest, through preprocessing, to model inference (i.e., use in the real world to make classifications or predictions). This role's responsibilities are closely related to those of the DevOps practitioner in software development.

Conclusion

In this post, we explored the job titles of Data Analyst, Data Scientist, and a few positions related to machine learning using the metaphor of a track team. The Data Analyst might start off the relay, before passing cleaned data to the Data Scientist for modeling. The Machine Learning Engineer is like an experienced coach, specialized in deep learning. Finally, the MLOps practitioner is like the bus driver responsible for getting the team to the track meet.

To learn more about other job functions, check out our article, Data Owner vs. Data Steward vs. Data Trustee.