Machine Learning Engineer vs Data Scientist - The Differences
Machine Learning Engineer vs Data Scientist - Battle of the Best Data Science Job Roles- Differences and Similarities Unleashed
Are you a newbie in the data science domain ready to embark on a rewarding journey but are confused between the roles of a Machine Learning Engineer vs Data Scientist? Many data science beginners do not clearly understand the two job roles and often find it challenging to understand the day-to-day roles and responsibilities revolving around these jobs. Data Science is an emerging discipline and so are the roles and job titles pretty much evolving. Hence, it is challenging to find a straightforward definition on the internet. Read this article to understand the significant differences and similarities between a machine learning engineer and a data scientist.
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- Machine Learning Engineer vs. Data Scientist - Who Are They?
- Data Scientist vs. Machine Learning Engineer Jobs and Growth Trends
- Machine Learning Engineer vs. Data Scientist - Roles and Responsibilities
- Machine Learning Engineer vs. Data Scientist - The Skillset
- Machine Learning Engineer Salary vs Data Scientist Salary
- Machine Learning Engineer vs Data Scientist -The Similarities
- Machine Learning Engineers vs Data Scientists - Top Job Roles in the World of Data Science
Machine Learning Engineer vs. Data Scientist - Who Are They?
Assuming that most people reading this article would have little or no knowledge about Data Science, let us start from scratch to contextualize the terms Data Scientist and ML engineer. Suppose you understand AI/ML and Data Science as a combination of two words. In that case, it will be better to interpret the meaning simplistically. Consider an AI/ML system as the combination of "Data" and "Code." Data Scientist is the person who works majorly on the data and, through research, decides what data should be fed into the system (Machine Learning model). In contrast, a machine learning engineer is a data professional who makes the AI/ML system available for a set of customers or organizations, ready to make predictions.
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Going by the technical definition, a Data Scientist is the person who is responsible for deriving meaningful insights from the data and building an accurate machine learning model, which will further help the company to nurture its business interests and dictate product requirements.
To define the role of a Machine Learning Engineer, they are the professionals who go one step ahead to push or integrate the machine learning model into a system and bring it into an existing production environment.
If you look at the machine learning project lifecycle, the initial data preparation is done by a Data Scientist and becomes the input for machine learning engineers. Later in the lifecycle of a machine learning project, it may come back to the Data Scientist to troubleshoot or suggest some improvements if needed. To give it a better technical context, ML engineers mainly work towards serving and packaging the machine learning model in an appropriate framework and scalable manner to make them available to end-users.
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View All ProjectsData Scientist vs. Machine Learning Engineer Jobs and Growth Trends
From an industry or employment perspective, Data Science is already taking a leap in all domains: IT, Healthcare, Pharma, E-commerce, Finance, etc. With the emerging big data revolution, the demand for data scientists and Machine Learning Engineers is ever increasing. More and more companies are beginning to foster data-driven technologies to solve challenging problems. Slowly many organizations are resorting to AI and machine learning-enabled software systems to allow extreme precision and prediction that outcompetes human intelligence.
As for the job prospects, both roles are emerging and attract a lot of opportunities, thereby creating an overwhelmingly high demand. Still, suppose you dig a little deeper to look individually. In that case, Data Science is a comparatively broader and generalist role than Machine Learning Engineer, which is quite a specialist role and, therefore, sees a lot more vacancies, according to Indeed.
Image source: Indeed
But if you look in terms of the abundance of jobs worldwide, in recent times, the need for ML engineer roles is growing at a much faster rate than Data Scientists, according to Linkedin. This hints that the trend for both positions is changing over the years. It wouldn't hurt to quote the reason behind this— ML engineer is an advanced specialized role and requires years of experience as a software engineer or data scientist. So, the journey of a machine learning engineer may begin as a software engineer, further as a data scientist, to senior data scientist to a lead role in data science, eventually blending in all the skills learned over time.
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Machine Learning Engineer vs. Data Scientist - Roles and Responsibilities
There is a bursting myth among many data science aspirants; they think it is all about Machine Learning. According to Harvard Business Review, 80% of the data scientists' work is data cleaning; the rest comprise model building and validation. On a funny note, the main goal of data scientists is one step ahead of Data Analysts, whose main job is to analyze data, observe exciting trends patterns and generate meaningful reports by employing statistical analysis and visualization. The data analyst roles are synonymous with data scientists in some organizations.
If you look more deeply, the work of a data scientist would be to explore the data and extract and identify the most important "features" which drive the power of prediction. They need to know everything about the data and apply various mathematical and statistical tools to identify the most significant features using feature selection, feature engineering, feature transformation, etc.
The job of a Machine Learning Engineer is to maintain the software architecture, run data pipelines to ensure seamless flow in the production environment. They are also expected to collaborate with appropriate business stakeholders to identify and analyze the requirements, scope, and hint resolution. Following production, their role is to reduce errors, improve the performance of the models, and deal with potential issues. Post-deployment stages require the combined effort of both machine learning engineers and data scientists. The work of ML engineers, in most cases, begins after the model building. In some cases, they might have to perform model building, deployment, and finally, model monitoring or optimization. In such cases, part of a data scientist's work may be a secondary task for the Machine Learning Engineers.
Analogywise, the profile of Data Scientists can be thought of as "creative" as it involves experimenting (research), just like an architect. In contrast, the role of an ML engineer can be related to that of a civil engineer who is responsible for driving and maintaining SOTA architecture and ensuring scrupulous predictions to counter growing data usage and accessory needs. The main aim of both roles is to solve challenging business problems by using "data" in the best possible manner.
Machine Learning Engineer vs. Data Scientist - The Skillset
Data Scientists and Machine Learning Engineers are expected to have a versatile skillset and a substantial overlap of skills. An essential skill for both the job roles is familiarity with various machine learning and deep learning algorithms. They also have to be adept at math & statistics, which form the backbone of data science and everything, from understanding the business requirements to data collection to algorithm selection to model building.
On the other hand, Machine Learning Engineers are expected to have vast knowledge and immense exposure to advanced linear algebra, statistics, data structures, NLP, and other aspects concerning fundamental software development. They are heavily focused on the technical and programming part without bothering much about the subject matter expertise that goes behind them.
Data scientists and ML engineers should have experience with popular machine learning frameworks like Tensorflow and Keras, libraries like Pandas, Scikit, Pytorch, etc. They should be highly proficient with Python, R, and Java/JS programming. Python or R is a must-know programming language as they invariably stand as the industry's choice for applying Machine Learning, be it writing code, implementation, or deployment. They should also have experience with ML model deployment using popular python-based frameworks like Flask, Fast API, etc.
The umbrella of an ML engineer constitutes many things, but this list is not exhaustive; it majorly covers the combined skills of a software engineer and data scientist in totality. It includes writing production-ready code, code review per the traditional software engineering best practices (SDLC), TensorFlow for model building, SQL for database development, Spark (for big data analytics), followed by maintenance of machine learning systems to reduce errors and improve predictive modeling, assessment of model performance, model optimization. They are also expected to have some amount of exposure with MLOps and popular cloud computing software like AWS, Microsoft Azure, etc., to provide cloud-based deployment if needed. So, to put it in brief, ML engineers should be familiarized with engineers who are expected to have a well-rounded experience with how to deploy the model, update the model, and scale them using all the standard tools. Additionally, they are expected to know operations and drive Auto ML systems.
Data Scientist roles are more research-oriented because they involve knowing in-depth theoretical and practical aspects of algorithm development and knowledge of various machine learning algorithms and even deep learning in some cases.
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Machine Learning Engineer Salary vs Data Scientist Salary
According to Payscale, the salary of Data Scientists lie between the range of $85K and $134K. On the other hand, machine learning engineers earn somewhere between $93K and $149K . These figures are purely survey-based and may vary from place to place, company to company. However, the salary of a entry level data scientist (fresher) in India with less than one year of experience ranges from almost 4.4 lakhs to 24.1 lakhs, with an average of 10.5 lakhs. In contrast, the salary of a Machine Learning Engineer with less than 1 year of experience ranges from ₹ 3.1 Lakh to ₹ 21.8 Lakh with an annual average of 7.0 Lakhs.
Image source: Payscale
Machine Learning Engineers are software engineers who acquire skills involving data science; hence, it is their hybrid skill set that gets them more paid. This doesn't intend to underestimate a Data Scientist's role in any way and get them entitled to a lesser pay. It's important to understand that, on average, Machine Learning Engineers, in general, are paid considerably higher as compared to data scientists. The difference in salary for the two roles can also be accounted for by the difference in engineering/science background. This may seem not very objective, but the statistics shall speak for themselves.
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If you look at a startup company, one wouldn't find a difference between the pay of these two roles. This is because, in startup culture, the roles are not much specific and stipulated. They can hire a person under the title of Data Scientist who will also have to work as a Machine Learning Engineer, ending up in a full-stack data scientist position, and in this way, they wouldn't really justify the difference in the salary.
Machine Learning Engineer vs Data Scientist -The Differences
A significant difference in the roles comes from the very background and position they come to hold in terms of an organization. Machine Learning Engineers are traditionally Software Engineers who have learned to use Machine Learning for problems that could not be solved using traditional computer science algorithms. This means that ML engineers usually have a Bachelor’s or Master’s in Computer Science, Information Technology, or relevant STEM disciplines with little or no research background. Ideally, Machine Learning Engineers can carry out the tasks of a Data Scientist, but vice versa is not possible.
Machine Learning engineers collaborate with software development and product management teams to support robust ML-based features and bring them into the production environment. They are the key players in bridging or gluing for the ML model to grow and turn out a great benefit to end-users in the long term. Look at the backgrounds of people who work as Data Scientists. You will find that they come from a diverse range of fields like statistics, mathematics, programming, bioinformatics, economics, etc. They usually hold a Ph.D., which means they mostly hail from a research background.
On the other hand, Data Scientists work in coordination with data analysts and data engineers. While the Data Scientists are busy analyzing the "data" and building the appropriate model, the machine learning engineer makes sure that the model environment can easily handle that large amount of data.
Machine Learning Engineer vs Data Scientist -The Similarities
Machine Learning Engineers and Data Science form an integral part of the Data Science team framework responsible for driving a company's success. Both of them work with big data. Hence, the biggest challenge and the most critical similarity they share requires exceptional data management skills. The roles of ML Engineer and Data Scientist may show considerable overlap in some cases, and this depends on a couple of factors. The distinction between the two job roles may be hard to define in most cases. This majorly depends on the company's size. The size of the project and team determine the outcomes and roles or responsibilities that ultimately decide the job description and hiring criteria. It varies from company to company. Suppose there is a big company/team/project. There could be different persons employed as ML engineers or data scientists. Still, if the company/team is small, the same person may undertake both roles.
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Machine Learning Engineers vs Data Scientists - Top Job Roles in the World of Data Science
To conclude, the Data Scientist is often seen as the "Masterchef." He figures out how to prepare a good meal wherein his crucial job is to clean the data, prepare the ingredients and precisely mix them. Their job is to continuously prepare high-quality meals capable of satisfying the needs of businesses that desire to serve the best-in-class and the customers waiting to savor the best experience. Further, the machine learning engineer will actually package, leverage, deliver, support, and operationalize, ensuring that it reaches their customers as is expected by them. However, it is worth pointing out that with the changing scenario of Data Science, the focus is increasingly shifting to data-centric approaches rather than model-centric strategies, as quoted by Andrew NG, a prominent Data Scientist cum mentor, in a recent article. This means that improving "data" has been proven to show a more significant impact on the predictive performance of a machine learning system rather than a tweak in "code."
This essentially aims to chunk down the existing roles of Machine Learning engineers and data scientists. With this shifting paradigm, the emphasis and reliability on the responsibilities and work of data scientists might stand on a higher edge than that of machine learning engineers and might be taken over by another set of game-changers in the future- "Data engineers." This might mean that the tables may change with time since it's still in an evolving phase. At the same time, an increase in demand might also mean a crunch of job hunting for highly skilled people. The changing roles and opportunities might become more challenging with time.
To conclude, in short, it cannot be accurately predicted what the state of Data Science will be in the future. Still, it is certainly promising as we learn to explore it. As long as it is evolving, it doesn't matter whether you start off as a Data Scientist or Machine Learning Engineer unless you are ready to adapt and get going.
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