As Artificial Intelligence business applications become commonplace, you may find yourself with the question of whether to pursue a Machine Learning Engineer vs Data Scientist hire or career decision.
The industry tends to confuse between the two job roles. However, while they share many of the skills required to do both jobs, there are subtle differences as well.
This post explores insights into what machine learning engineers and data scientists do, similarities and differences between the roles and salaries. Also, we discuss how to decide which one to pursue.
In this article:
Machine Learning Versus Data Science
Before comparing machine learning engineer vs data scientist job roles, let’s explain what machine learning (ML) and data science are.
Towards Data Science, a leading web publication provides an excellent definition of what data science is: Data Science, at its most basic level, is a complex combination of skills to analyze and obtain insights, information, and value from vast amounts of data. It aims at finding hidden patterns and then use them to make business decisions.
On the other hand, Machine Learning focuses on extracting information from databases, finding the best model representing it, and then teaching machines to make decisions or perform tasks based on them. For example, engineers can teach computers to recognize fraudulent transactions, determine the best selling price, recognize traffic signals on a video image, and many more applications.
The two fields are interdependent; In machine learning application development, the data science discipline is responsible for identifying the best prediction models. Reciprocally, data scientists can also make use of machine learning as a tool.
Data Science vs Artificial Intelligence
Machine Learning in itself is a subfield of Artificial Intelligence, hence the data science vs AI connection.
As explained before, engineers build machine learning models from data. Data Science uses statistical insights to find the best model. Afterward, machine learning engineers implement that model in a computer system that can make predictions or perform tasks.
Data science intervenes in several critical machine learning steps and is instrumental in achieving its goals.
For more information about data science, machine learning and the overlap between them, check out our post: Data Science vs AI Explained for the Business.
Machine Learning Steps
To visualize the role that machine learning engineers and data scientists play in an organization, we need to understand the project development process they cooperate with.
Most of the time, machine learning projects follow these steps:
- Problem definition.
- Data collection and preparation.
- Model development.
- Model testing.
- Machine learning service deployment.
- Machine learning service management.
This set of steps is also known as the Data Science Lifecycle. Check out our blog post on 6 Machine Learning steps explained for the business for a detailed look.
Data Scientists and Data Engineers are critical in steps 1 through 4. Machine Learning Engineers are often heavily involved in 5 and 6.
Let’s explore each role in detail.
What Does a Data Scientist Do?
Today’s organizations are gathering and deriving insights from data at unprecedented levels. As a result, companies are increasingly using data-driven approaches to answer questions or solve a problems.
Until recently, companies would hire Data Analytics or Business Intelligence professionals to gather, process and derive insights from data.
Data Scientists are the newest addition to the team. Their task is to take data-driven organizations to the next level, building models that predict rather than explain behavior.
Data Scientists combine skills from many professional fields, including statistics, computer science and analytics. To sum up, its ultimate goal is to help businesses find potential hidden revenue and efficiencies.
Data Scientists Typical Tasks
Some of the typical tasks that Data Scientists do are:
- Problem Framing: This involves asking yourself who your clients are and what are their information needs. In other words, for this part of the job, Data Scientists need to collaborate in multidisciplinary teams integrated by product managers, engineers and other company departments. The ultimate objective is to understand the company’s needs better and therefore come up with better-tailored solutions.
- Data Collection: In almost every organization today, data is distributed among several interconnected computer systems. To collect data effectively, Data Scientists must be proficient in database programming languages, such as SQL, which is the most widely adopted. Also, it helps to be well-versed in emerging database paradigms, such as NoSQL. Besides, they must be versed in Data Mining tools for data extraction and transformation.
- Data Preparation: Data from transactional systems is seldom usable right out of the box. Data Scientists will almost always have to check data for missing fields and inconsistencies. Also, they must combine and aggregate information to construct the information you need almost always. Again, knowledge of database programming languages and data mining software is necessary.
- Statistical Analysis: This entails analyzing the data to identify patterns and trends. To accomplish this, they research and develop statistical models for analysis and therefore require advanced knowledge of maths and statistics. Also, knowledge of statistical software tools like SAS, SPSS, MATLAB, is paramount.
- Predictive Modeling: This is an essential part of the job. It involves applying cutting-edge knowledge in algorithms, statistics and machine learning to identify insights and predictions.
- Communicate Results: Data Scientists spend a significant portion of their time presenting their results to stakeholders. Interpersonal and communication skills are critical. Also, data visualization tools are essential to present findings.
Data Scientists Skills and Requirements
According to payscale, the most sought-after skills in a Data Scientist are Machine Learning, Python, Data Analysis and Statistical Analysis.
Also, job postings at websites like Indeed often mention the following as requirements for Data Scientists:
- Proficient in SQL programming.
- Skilled in Python or R programming.
- Knowledge of quantitative analysis and statistical techniques.
- Familiarity with data mining techniques and tools.
- Proficient with statistical software tools (E.g., SAS, SPSS).
- Writing and communication skills.
- Ability to work in a multidisciplinary environment and communicate with people in diverse departments, such as IT, DevOps, Project Leads, company executives, business department leads and many others.
- Working knowledge of cloud tools and technologies, such as Amazon Web Services, Azure and Google Cloud.
Data mining is an essential skill for data scientists, check out this article for some examples of this technique: 15 Examples Of Data Mining In Real Life: Uncovering Hidden Insights.
Of course, there are many other skills that the Data Scientist needs. His job definition is often highly dependent on the business domain area or specific industry problem his employers are trying to solve.
What Does a Machine Learning Engineer Do?
In an online blog post, Andrew Zola offers us an excellent definition of the job:
Machine learning engineers sit at the intersection of software engineering and data science.
The overall objective of Machine Learning is to teach a computer system to perform a specific task. As such, Machine Learning Engineers collaborate with data scientists, computer engineers and developers to create programs that allow machines to function without direct human assistance.
Machine Learning Engineers take theoretical Data Science models and develop them into production-level solutions to handle vast amounts of real-time data. To this end, they use programming frameworks, big data and machine learning software tools, hosted on-premises and in the cloud.
Machine Learning Engineers’ Most Frequent Tasks
Here are some of the specific tasks that Machine Learning Engineers do:
- Architect, design, and implement large-scale and enterprise-grade machine learning models and AI solutions, using ML tools and software libraries.
- Work closely with data engineers, data scientists, software developers, product owners and business representatives to:
- Define requirements.
- Design featured-oriented data-driven solutions.
- Discover, analyze, structure and mine data.
- Develop data and model pipelines.
- Translate analyzed data into working machine learning (ML) models.
- Feed data, train and evaluate ML and statistical models defined by Data Scientists.
- Perform data extraction and management.
- Ensure that ML programs operate according to specifications.
- Implement machine learning algorithms and libraries.
- Developing neural network models supporting business use cases.
- Maintain ML and AI solutions in an enterprise setting, Improving existing models.
- Refine, deploy to production and scale ML models.
- Integrate ML models into consumer products.
- Develop and maintain reusable tools and frameworks to automate the data science lifecycle.
- Participate in the broader computer engineering life cycle to design distributed systems, write production code, develop data pipelines and perform code reviews.
- Build programs that control computers and robots.
- Communicate complex processes to business leaders.
Machine Learning Engineers Skills and Requirements
Machine Learning Engineers must be proficient in data science, statistics and software engineering.
According to Payscale, the skills that employers frequently look for when hiring Machine Learning Engineers are Python, Deep Learning, Computer Vision and Natural Language Processing.
Machine Learning and Python are skills also demanded of Data Scientists, however other like Deep Learning, Computer Vision and Natural Language Processing are related to Artificial Intelligence (AI) Engineering applications. Both Data Science and AI Engineering projects often require Machine Learning Engineers.
Job postings at Indeed also reveal more skills expected from Machine Learning Engineers, such as:
- B.S. degree or above in Computer Science, Electrical Engineering, Machine Learning, Operation Research, Statistics, Data Science or related fields.
- Knowledge of extracting and processing data with SQL and NoSQL.
- Proficient in one or more programming languages (E.g. Python, Scala, Java, C++).
- Knowledge of libraries for data analysis and machine learning (Scikit-Learn, Scipy, NumPy, Pandas, Matplotlib).
- Skills in public cloud machine learning tools from AWS, Azure and Google Cloud.
- Familiarity with Machine Learning topics, such as supervised learning, unsupervised learning, dimensionality reduction, tree-based models and optimization algorithms.
- Understanding of data mining algorithms and statistical methods.
- Excellent communication skills to work in a multidisciplinary environment that includes Data Scientists, Data Engineers and Business Analysts.
- Experience with BigData ecosystem, data pipelines and ETL (Hadoop, Hive, Spark/PySpark).
- Production integration experience.
ML Job-Specific Skills
There are many more skills that individual job offers may demand. Some of them are very specific to the solution developed by the prospective employer, such as:
- Experience with one or more Container-ecosystem (Docker, Mesos, Kubernetes). An employer may demand this if its software development team works with containers.
- Experience in Natural Language Processing/Understanding using deep neural networks ( RNN (LSTM, GRU) / CNN), if the prospective employer is developing NLP solutions.
- Experience in working with GPU, profiling/low-level optimizations, Cuda/CuDNN, if the employers are using Graphical Processing Units (Such as NVIDIA GPU’s).
What Is the Difference Between Machine Learning Engineer vs Data Scientist?
In his article, Andrew Sola cites some expert opinions on the differences between Machine Learning Engineer vs Data Scientist.
- Machine Learning Engineers are more involved in writing production-level code. Simultaneously, data scientists analyze the data and determine the best model to integrate into that code.
- Data Scientists translate a business problem into a technical problem and develop a technical solution (The model). Then, the Machine Learning Engineer takes the model and integrates it with the company’s software platform, coding Application Program Interfaces (APIs) to be “served” or consumed.
- Machine Learning Engineers monitor solutions in production environments to ensure their integrity and quality. On the other hand, Data Scientists are less involved with production-level solutions.
- You can view a Data Scientist as a statistician who also knows a lot about programming. However, when compared to software engineers, they know more about statistics than coding. In short, Machine Learning Engineers bridge the gap between data and computer science.
Machine Learning Engineer vs Data Scientist: An Example of the Two Roles
To understand the difference between the roles better, let’s see an example. Imagine that you are working for an online retailer that is developing a recommendation system for its shoppers:
- First, a Data Scientist would analyze the data and determine the best model to make predictions on the next product a given customer is most likely to buy.
- Then, the Machine Learning Engineer would configure that model or algorithm, developing in-house code or using public cloud services like AWS, Azure or Google Cloud.
- Finally, the ML Engineer would make the model available in a REST web service that the online store developers can call to get product recommendations.
Data Scientists are well-equipped to store and clean data, explore data sets, identify insights and build predictive models. In contrast, Machine Learning Engineers may provide the tools or environment that Data Scientists need to work. They may also implement the insights discovered by them into production computer systems that can predict behavior or perform specific tasks.
Machine Learning Engineer vs Data Scientist Salary
Another factor for deciding on a particular job role or employee profile is average salaries. Following, we share the average earnings in the United States for Machine Learning Engineers and Data Scientists according to three different sources: Glassdoor, Payscale and Indeed.
Machine Learning Engineer Yearly Salary According To:
Yearly salaries often depend on different factors, such as city or state and years of experience.
Entry-level Machine Learning Engineers often start with yearly earnings of around $93K. Moreover, experienced professionals can earn over $180K a year.
Data Scientist Yearly Salary According To:
Entry-level Data Scientists often start with yearly earnings of around $89K. Furthermore, Senior-level professionals can earn over $143K a year.
Should I Learn Data Science Before Machine Learning
The short answer is Yes! Let’s examine the reasons:
- Machine Learning Engineering and Data Science share many concepts, methods and tools, such as data mining, analysis, statistical modeling and algorithm development. Both roles need to learn many of the same skills.
- Machine Learning Engineers work closely with Data Scientists (most of the time). Therefore, they should learn the same theoretical concepts, practice methods and tools to develop a common language.
- Since Artificial Intelligence is new to business applications, most organizations have not defined specialized roles for both Data Scientists and Machine Learning Engineers. If you scan job offers, you will notice that organizations are not yet very good at distinguishing between the two roles (They assume they are the same). Many companies also require professionals to wear many hats as new ML departments are relatively new and experimental.
Can a Data Scientist Become a Machine Learning Engineer
The answer is Yes! But they will have to learn new skills in the computer systems engineering and development discipline.
A good Data Scientist should already be proficient at data extraction and wrangling methods using programming languages like SQL and Python. Furthermore, he or she is also knowledgeable of statistical analysis and may even have learned about Machine Learning algorithms.
To become a Machine Learning Engineer, they would need to learn the programming tools and computer systems to deploy Machine Learning services in a production environment. These may include some of the following:
- Programming languages to develop production web services, such as Java, C++ and C sharp.
- How to configure and deploy solutions in Machine Learning cloud services by providers like AWS, Azure and Google Cloud.
- Using BigData ecosystem, data pipelines and ETL (Hadoop, Hive, Spark/PySpark).
- Container-ecosystems. If your company software development teams use containers.
- Specific AI Engineering solutions for varied fields, such as Natural Language Processing/Understanding, Computer Vision and Deep Learning.
Machine Learning Engineer vs Data Scientist: Do You Have to Choose?
In this article, we have showcased what Machine Learning Engineers and Data Scientists do. Also, we highlighted the similarities and differences between them.
Moreover, we established what each professional could do for your organization and how they complement each other in your teams.
We invite you to take action now. Machine Learning and Data Science can power up your business and bring it to the next level. Consequently, create a Machine Learning or Data Science department within your company and hire a professional team to help you.
If you would like to know more about machine learning and data science, check out our article about Data Science vs AI.
Also, check out our article 6 Machine Learning Steps explained for the business for more insights into how to integrate Machine Learning into your business.
What are your thoughts on the job role of Machine Learning Engineer vs Data Scientist? Please leave a comment below.
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Machine Learning Engineer vs Data Scientist References
Andrew Zola. Machine Learning Engineer vs. Data Scientist.
Career Explorer. What does a data scientist do?
Hugo Bowne-Anderson. What Data Scientists Really Do, According to 35 Data Scientists.
Indeed. What does a Machine Learning Engineer do?
Raj Bandyopadhyay. The Data Science Process: What a data scientist actually does day-to-day.
