Machine Learning Engineer vs Data Scientist: 3 Critical Differences

on Data Driven, Data Extraction, Data Processing, Data Science, Data Visualization, Machine Learning • July 13th, 2021 • Write for Hevo

Machine Learning Engineer vs Data Scientist | Hevo Data

There is a pent-up demand by enterprises to make data-driven decisions these days and as such, there is an increased value placed on those whose job description has anything to do with data as they are in high demand.

There are several existing and emerging roles as regards the Data Industry and understanding the specifics and requirements of each can go a long way in helping you build a successful career path and also help organizations looking to recruit Data Analysts to know exactly who or what they are looking for to fill such roles. 

As there is an emergence of lots of interest in jobs in the Data Industry, this article will help you in getting a deeper insight into the discussion of Machine Learning Engineer vs Data Scientist, highlighting their responsibilities within an enterprise, and showcasing the skills associated with each of them.

Table of Contents

What is Data Science?

Data Science has taken a significant role in the Data Industry as it has become one of the most valuable assets of any enterprise wishing to grow and do business efficiently in the 21st century. Data Science is a field of study that combines Programming Skills, Knowledge of Mathematics, and Statistics to extract relevant deductions from data. It can further be described as the prediction and inference of data from both Structured and Unstructured data to help individuals and enterprises come up with better decision-making to serve their customers.

Data Science deals with extracting data from its origins, knowing what the data represents, and how it could be transformed to become an asset that will give valuable information to a business by identifying useful patterns. It does this through disciplines like Computer Science, Mathematics, and Statistics, and employing techniques such as Data Mining, Cluster Analysis, Visualization, Machine Learning, etc.

Data Science has become important today as more companies are churning out large amounts of data, they have realized that this data produced can act as a spring-board to enable them to get insights into behavioral patterns of customers, know where lapses occurred to fix them, see clients purchasing strengths as regards priorities, etc. Regardless of the size or industry of an organization, analyzing data to remain competitive is now an attractive proposition.

What is Data Scientist?

A Data Scientist is one charged with the responsibility of analyzing Structured and Unstructured data to generate information and gain valuable insights from them which in turn will be used in problem-solving, answering questions, and predicting outcomes.

The Data Scientist carries out the analysis by Testing, Aggregating, and optimizing data using Statistical models, Predictive, and Prescriptive Analysis to come up with inferences that will be presented to individuals, boards, team members, etc. to help in their overall decision-making process.

Data Scientists usually have a background in Mathematics and Statistics. 

What is Machine Learning?

Machine Learning is a subset of Artificial Intelligence (AI) which is a part of Data Science. This branch of AI produces a class of data-driven algorithms that enables software applications to become highly accurate in predicting possible outcomes without any need for explicit programming. 

Machine Learning involves the development of algorithms that can receive historical data as input and leverage statistical models to predict new output values, the algorithm also updates outputs as new data values become available. 

The importance of Machine Learning cannot be overstated as it is a significant differentiator for many companies hence, many enterprises are investing heavily to have a view of trends in customer behaviors, business operational patterns,  and for the development of new products using facts gotten from the history of previous products. 

Machine Learning has become an integral part of leading corporations and has formed a central part of their operations as it can be used for Fraud Detection, Spam Filtering, Predictive Maintenance, Malware Threat Detection, and Business Process Automation, etc.

What is Machine Learning Engineer?

A Machine Learning Engineer is saddled with the responsibility of creating software programs and algorithms that allows computers to perform functions without being directed or told what specific steps to undertake or tasks to execute. 

Machine Learning Engineer relies heavily on their programming skills as they are meant to train machines in such a way that they can function with the knowledge they have acquired to perform specific tasks without interference or intervention. 

Machine Learning can be Supervised, Unsupervised, or Semi-Supervised. Reinforcement Learning is also an important classification of Machine Learning. 

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Machine Learning Engineer vs Data Scientist

Data Science and Machine Learning have been defined to reflect their unique properties but the purpose of this section is to give you an in-depth insight into the discussion of Machine Learning Engineer vs Data Scientist, so as to further elucidate and show the difference that exists between the two job roles, the subsequent sections will further define these roles then look at their responsibilities and the requirements needed in both of them and finally, their salary regime. 

Machine Learning Engineer vs Data Scientist: Responsibilities

Data Scientist

A Data Scientist is responsible for assisting companies to make data-driven decisions. They gather, process, and derive relevant insights from data to answer questions or solve problems as required by an organization. They explore all aspects of the business data and develop complex models using various technologies to perform Data Analysis that will drive the productivity of the firm. The responsibilities of a Data Scientist include:

  • Acquiring and Collecting data from multiple data sources by setting up Data Collection Architectures.
  • Processing and Cleaning of data before storage. 
  • Researching and developing Statistical models for Data Analysis.
  • Understanding the needs of both consumers and company, then coming up with models to help cater for their respective needs.
  • Initializing data investigation and analysis using Data Science techniques.
  • Communicating findings, concepts from research, and analysis to relevant individuals, bodies, teams, or key decision-makers to allow for planning and implementing strategies derived from the analysis.
  • Identifying the latest trends and opportunities in the industry and coming up with design models that will aid the overall improvement of the business.
  • Building processes and tools to help monitor and analyze performance and data accuracy. 
  • Enhancing customer experience, revenue generation, ad-targeting, etc. by using predictive models.

Machine Learning Engineer

The Machine Learning Engineer can be seen as the intermediary between the software engineer that builds a Data Pipeline and the Data Scientist who carries out the analysis of the data, they ensure that data coming through the pipelines are redefined into Data Science models. They build programs that control computers and robots by developing algorithms to enable the computers to teach themselves and aid them in understanding commands. A Machine Learning Engineer performs the following functions:

  • Designs Machine Learning models.
  • Builds software that supports Machine Learning Applications.
  • Develops and trains models on how to interpret data by using different parameters.
  • Understands Statistical principles such as probability and distributions to run the Machine Learning algorithms.
  • Collaborates with Data Engineers to build and integrate Data Pipelines.
  • Improves existing Machine Learning models by writing production-level code and reviewing them constantly to better the process.
  • Runs Machine Learning tests, carry out experiments on them, and performs Statistical Analysis.
  • Ensures reliable connectivity between databases and the back-end systems to enable the proper flow of information.
  • Implements Machine Learning algorithms and libraries.  

Machine Learning Engineer vs Data Scientist: Skills Required

Data Scientist

Data Scientists are usually highly educated as most of them have acquired a master’s degree or a Ph.D. in Computer Science, Mathematics, or Statistics, though there are numerous industry skills one can leverage to become a successful Data Scientist. These skills include:

  • Knowledge of Java, Python, and SQL.
  • Strong Mathematics and Analytical skills. 
  • Experience in Statistics and Data Mining techniques such as Linear Regression models, Boosting, Random Forest, trees, etc.
  • Knowledge of advanced Statistical methods and concepts.
  • Knowledge of Machine Learning techniques like Artificial Neural Networks, Clustering, and Decision Trees.
  • Experience using Web Services such as DigitalOcean, Redshift, S3, and Spark.
  • Working with distributed data and computing tools such as Hadoop, Hive, MapReduce, Spark, MySQL, etc.
  • Knowing how to use AdWords, Facebook Insights, Google Analytics, Hexagon, etc.

Machine Learning Engineer

Just like the Data Scientist, most establishments prefer Machine Learning Engineers to have a master’s degree or a Ph.D. in Computer Science, Engineering, or its related fields. A successful Machine Learning Engineer will need to be acquainted with the standard implementation of Machine Learning algorithms available through APIs, libraries, and Machine Learning packages. Other skills needed include:

  • Programming Languages like Python, Java, R, C++, C, JavaScript, Scalia, Julia, etc.
  • Experience in Computer Vision, Natural Language Processing, Deep Neural Networks, and Gaussian Processes.
  • Strong Mathematical, Engineering, and Analytical background. 
  • Ability to design and develop Machine Learning systems based on open-source Machine Learning frameworks, SDKs, and libraries. 
  • Understanding probability and statistics, and working with distributed systems.
  • Knowledge of Programming Tools like MATLAB, and Linux SysAdmin.
  • Working with large amounts of data, especially in a high throughput environment. 
  • Capability to engage in continuous testing, validation, and versioning. 
  • Knowing how to use GitHub/Git to store code repositories.
  • Knowledge of Machine Learning evaluation metrics and practices. 
  • Knowledge of deployment tools like AWS, Google Cloud, Azure, Docker, MLFlow, Airflow, etc.

Machine Learning Engineer vs Data Scientist: Salary

Data Scientist

Data Scientists are in high demand as many corporations are investing heavily in insights that could be derived from the data they produce and as such, the average salary of a Data Scientist is about $120,000 per annum though, this depends on the nature of the job at hand.

Machine Learning Engineer

Just like the Data Scientist, companies are looking for Machine Learning Engineers who can develop algorithms to enable computers to learn and detect patterns on their own that will, in turn, improve the overall health of the business. The average salary of a Machine Learning Engineer is around $145,000 annually and depends on the corporation offering the job.

Conclusion

This article gave you a holistic understanding of the discussion around Machine Learning Engineer vs Data Scientist, detailing their respective roles and requirements. As can be noted, there is an overlap of duties between both job roles and they can be found working on similar projects as they are involved with data and trying to develop methods in manipulating data to unlock trends and patterns. 

The Data Scientist though is more involved with the analysis of the data which has been structured into recognizable formats whereas, the Machine Learning Engineer is more interested in developing and training models that computers will use to perform tasks without interference. 

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