Will Machine Learning engineers replace data scientists?

Data scientists and machine learning engineers are in demand because they have the skills and expertise to work with large and complex datasets and extract meaningful insights from them.

Data science and machine learning have applications in a wide range of industries, from finance and healthcare to marketing and e-commerce.

Role and Responsibilities of a Data Scientist

A Data Scientist will collect, analyze, and model data and the end goal is to extract insights from data that will help a business.

Key responsibilities of a Data Scientist include:

• Researching and investigating the business problem.
• Communicating with the management and other members of the team to understand the issues better, and create strategies.
• Gathering data
• Process and clean data
• Data mining and extracting information from the data
• Visualization of data
• Discover trends and patterns

• Analyzing large/complex structured/unstructured data
• Developing systems/models and ML algorithms.

The role and responsibilities of a data scientist vary across various domains and can differ from company to company.

Skills of a Data Scientist

• Maths, Statistics, and Probability
• Programming with Python
• Knowledge of Python libraries like Numpy, Mathplot, Pandas, etc.
• R programming
• SQL
• Excel
• Hadoop and similar tools
• Cloud Computing concepts
• Data visualization tools(Tableau, Microsoft Power BI)
• Familiar with Machine Learning, Artificial intelligence, learning, and deep learning.
• Soft skills (Communication, business skills, Decision Making, Storytelling, etc)

Role and Responsibilities of a Machine Learning Engineer

Machine Learning is a branch of AI. ML algorithms result in systems that can predict or decide on new, unseen data.

Some examples of Machine learning in action include personalized recommendations on NetFlix or any other platforms, Fraud detection, and Image classification because of machine learning algorithms. Machine Learning Engineers build, design, and maintain AI systems.

Key responsibilities of a Machine Learning Engineer include:

• Identify, and research business problems
• Communicate and discuss with team members to understand the problem better.
• Preparation of data
• Build AI/ML systems according to needs.
• Problem-solving and applying appropriate ML tools and algorithms.
• Training, retraining, and evaluating model performance.
• Optimizing model performance.
• Integrating, deploying, and maintaining models.

Skills of a Machine Learning Engineer

• Mathematical/statistical knowledge
• Good programming skills(Python, Java, C++, R)
• Data modeling
• Knowledge of software design, software engineering/ computer science
• NLP
• Neural Networks
• Machine Learning Algorithms
• Soft skills (Problem-solving, critical thinking, desire to keep learning, communicating, and collaborating with team members)

While these skills are just a broad overview, a data scientist and a machine learning engineer can possess a wide range of skill sets apart from the above.

Similarities and Differences

(These depend on different domains and companies)

What are the similarities between Machine Learning Engineer and a Data Scientist?

Both ML engineers and Data Scientists somewhat possess similar skill sets. Both have a background/knowledge in mathematics and statistics, good Python programming skills, write custom algorithms, and an understanding of machine learning/artificial intelligence and predictive modeling. Both may work in the same team or collaborate according to needs. There is an overlap in the skills and responsibilities of the two.

What are the differences between Machine Learning Engineer and a Data Scientist?

Machine Learning engineers are mostly software engineers who also have knowledge of other programming languages apart from Python, for example, C++ or Java. Data scientists mainly focus working on researching, analyzing, and processing data and may also involve in developing models.

ML Engineers focus on developing Training, evaluating, Optimizing model performance, and Integrating, deploying, and maintaining models.

How do data science roles vary from company to company?

The field of data science is continually changing and developing.

• Many roles under data science have different names but similar responsibilities, creating a lot of confusion for aspiring data science professionals.
• It's confusing for aspiring data professionals to look at the various job roles within data science and decide what they should pursue.
• Focusing on a set of skills that recruiters are looking for becomes helpful.
• The different roles exist because of the different ways companies use data.
• The type of company and the company size also play a vital role in hiring different data professionals.
• For example, many companies who do not work with AI/ML may not look to hire an ML engineer and may only require a data analyst/engineer/scientist. On the other hand, an MNC may need many data scientists/engineers/analysts and ML engineers.
• A smaller company may expect a data analyst/engineer to also have the skills of a data scientist.
• Some data scientists may possess the skills of an ML engineer and vice versa.

Hence, different companies have different definitions, of what they want in data professionals they are looking to hire for a specific role.

Demand for Data Scientist vs Machine Learning Engineer

ML Engineer

According to a Venture Beat article, “87% of data science projects never make it into production”, this shows the need for ML engineers in a company.

The role of an ML engineer is comparatively new when compared to data scientists/analysts. Therefore the demand for ML engineers will increase even more in the future because AI/ML is only in the adoption stage for many companies.

Data Scientist

The Bureau of Labor Statistics estimates that there will be a 36% increase in data scientist jobs (2021-31). The need for data scientists is expected to rise in the future. The future will demand skilled data professionals. Many tasks may get automated, but data scientists who continue to evolve and upskill will stay.

Are Machine Learning engineers replacing Data Scientists?

• As we discussed earlier, the definitions of the different roles under data science vary across industries and companies.
• In many cases, ML Engineer is the next level for a Data scientist.
• With that said, we have a world that has data scientists working on ML projects, and we also have a world that consists of data scientists working purely on complex data sets and analytics.
• Software engineers who have transitioned into data scientists with good programming skills may further enter ML engineering.
• In big ML companies, all the research done by a data scientist is put into action by an ML engineer. These data scientists can deliver effective results in collaboration with an ML engineer. An ML engineer will build models and deploy them in production.

Role of AI in Data Science

• AI is constantly evolving, and a lot of the analytics part may get automated. The work of a data scientist may get even easier in the future.
• Data Scientists of the future may look different and may have a different set of skills.

Will ML engineers replace Data Scientists?

• Not every task will be handled solely by an ML engineer. A data scientist and an ML engineer will collaborate and work to address complex business challenges.
• However, if data scientists have some skills similar to ML engineers, they can easily upskill in the worst-case scenario. They can also look to switch to ML engineer in the future.

Conclusion:

It was previously difficult to distinguish between a data scientist and an ML engineer, but now there is a more defined distinction between the two roles. However, the specific skills that are expected from data professionals may vary depending on the industry, company size, and type of organization.

Three roles that may dominate the data world include Data Engineers, Data Scientists, and ML Engineers.