Machine Learning Engineer vs. Data Scientist: Career Path

Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. Machine learning is a subset of artificial intelligence. This refers to machines being able to learn by themselves without being explicitly programmed. It is an application of AI which enables systems to learn and improve from experience automatically. While working with machine learning, various sets of algorithms are required. These algorithms use a set of training data to enable computers to learn.

Data scientist and machine learning engineer jobs are the two current hottest jobs in the industry and for a good reason as data has become part and parcel of our lives today, and massive amounts of data are being generated both by individuals and businesses across all industries. In this article, we will analyze the career paths of data scientists and machine learning engineers.

Who is a Data Scientist?

A data scientist makes decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. He is responsible for scoping out the right questions from the dataset. He works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown. Some of the techniques used by data scientists are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.

Who is a machine learning engineer?

A machine learning engineer is a sophisticated programmer who develops systems and machines that can learn and apply knowledge with specific instructions or guidance. They create programmes that aid machines in taking actions without being specifically directed to perform those tasks. Artificial intelligence is the primary goal of a machine learning engineer.

Data Scientist vs Machine Learning Engineer- Common Skillsets Needed

The common skillsets needed are:

  • Statistics– It is defined as the study of the analysis, interpretation, collection, organization, and presentation of data. Data scientists and machine learning engineers must know statistics to a great extent. They must be familiar with matrices, vectors, and matrix multiplication.


  • Data Cleaning and Visualization– Data cleaning or data cleansing is a valuable process that helps companies save time and increase efficiency. It is crucial to be able to tell a compelling story with data to keep your audience engaged and get your point across. Findings must be in such a way that they can be identified easily and quickly. Otherwise, it will be difficult to get it through to others. Data visualization has a make-or-break effect when it concerns the impact of your data.


  • Machine Learning and Neural Network Architectures– Machine learning and predictive modelling are two of the hottest topics. Both data scientists and machine learning engineers must know techniques such as decision trees, logistic regression, and supervised machine learning, etc. These will aid in solving different analytical problems based on the predictions of major organizational outcomes.


  • Computer Vision– Machine learning and computer vision are two core branches of computer science that function and power sophisticated system relying on computer vision and machine learning algorithms exclusively. But when both are combined, you can achieve even more.


Some of the specific skills a machine learning engineer must have are applied mathematics, software development, signal processing techniques, and language, audio, and video processing. The specific skills pertaining to a data scientist are effective communication, creative and critical thinking, and decision-making and problem-solving skills.

Roles and Responsibilities

Let us now analyze the roles and responsibilities of a data scientist.

  • Obtain data and recognize strength.
  • Perform market research.
  • Building and optimizing classifiers and selecting features using machine learning techniques.
  • Finding correlations, trends, and patterns in complicated datasets.
  • Identifying new trends for process improvement.
  • Cleansing, processing, and verifying the integrity of the data that is used for analysis.

We will now look at the various job roles of a machine learning engineer.

  • Studying and transforming data science prototypes.
  • Designing machine learning systems.
  • Developing machine learning applications based on requirements.
  • Selecting appropriate data representation methods and datasets.
  • Running machine learning tests and experiments.
  • Extending existing machine learning frameworks and libraries.

Career Path

We will now list down the various job titles or designations a machine learning engineer can opt for.

  • Machine Learning Consultant.
  • Machine Learning QA Engineer.
  • Senior Data and Machine Learning Engineer.
  • Machine Learning Analyst.
  • Machine Learning Engineer- Deep Learning and Natural Language Processing (NLP).

Let us now understand the job titles or career paths available for data scientists.

  • Data Scientist- Client Operations.
  • Senior Data Engineer.
  • Lead Data Scientist.
  • Data Analyst.

With companies such as Walmart, Amazon, indeed, IBM, Microsoft, Nvidia, and JPMorganChase always being on the lookout for skilled data scientists, choosing to become a data scientist is indeed a great decision for any professional who is looking to land lucrative jobs. The same is the case for machine learning engineers. Technology giants such as Adobe, IBM, Indeed, Dropbox, UBER, Walmart, Chase, and Redhat are constantly hiring machine learning engineers for their businesses. Take your career to the next level by equipping yourself with the adequate skill sets that will help you make your mark in the fields of machine learning and data science. It is important to constantly be in tune with the advancements in technology to shine in your career. To stay ahead of the rest of the world and get certified in data science and machine learning, enrol in the comprehensive certifications offered by online learning platforms such as Global Tech Council and let your skills and certifications do the talking in job interviews.


Considering the fact that 2.5 Quintillion bytes of data being generated each day, professionals like big data experts, machine learning engineers, and data scientists are certainly the need of the hour as businesses need people who can make sense out of the data generated and organize this humongous data efficiently to provide business solutions. They can even be called the ‘heroes of the data sphere!’  With such demand for data scientists and machine learning engineers, there will never be a dearth for jobs in the data sector.

To learn more about certifications, check out Global Tech Council.