Data Scientists vs Machine Learning Scientists: Career Differences – Analytics Insight



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September 24, 2021

Data Scientists

Whether you’re just starting out in the workforce, have lately been laid off, is concerned about retaining your present job, or have been momentarily furloughed and have some spare time, there’s no better opportunity than now to learn some AI-related skills.

Machine learning and AI employment have risen 74% over the last four years, according to LinkedIn. Data scientists and machine learning scientists are among the job titles in this area, but you’re not alone in being puzzled about the distinctions between the two.

Today, we will learn about the career difference between Data Scientists and Machine Learning Scientists

What is Data Science?

The in-depth examination of enormous amounts of data housed in a company’s or organization’s archive is what data science is all about. This study includes understanding where the data originates from, assessing its quality, and identifying if the data will be used to assist future corporate development.

The data of a company is usually in one of two formats: organized or unstructured. We get helpful insight into the industry or customer dynamics when we study this data, allowing the company to gain a competitive advantage over its competitors by detecting trends in the data gathering.

Data scientists are specialists in turning unstructured data into useful business information. Algorithmic programming, and also data processing, artificial intelligence, and statistics, are all recognized by these experts. Amazon, Netflix, the healthcare industry, fraud prevention, web research, and airlines are all big users of data analytics.

What is Machine Learning?

Machine learning is a field of computer science that allows machines to learn without having to be programmed explicitly. Machine learning refers to the use of algorithms to evaluate data and make predictions without the need of humans. As inputs, Machine Learning uses a sequence of instructions, information, or observations. Companies like Facebook, Google, and others utilize machine learning extensively.

Difference Between Data Scientists and Machine Learning Scientists

Although these two jobs are occasionally identical among recruiters, if you specialize in any of these tasks, you are aware that there is a distinction. Despite the fact that both professions rely on machine learning algorithms, their day-to-day tasks might be rather different. Machine learning scientists specialize in use cases such as signal processing, object identification, automobile/self-driving, and robots, whereas data scientists work on use factors like fraud detection, product categorization, or customer segmentation.

Data Scientists

Data scientists might see more standardized job descriptions, as well as the education and abilities that are needed of them. A typical data scientist will identify a problem, generate a dataset, evaluate several machine learning algorithms, produce results, then analyze and communicate those results in collaboration with a stakeholder. The focus of data scientist positions is on business and stakeholder cooperation. They construct models in a fraction of the time it takes a machine learning expert, perhaps a few months or even weeks, depending on the job.

As a data scientist, you can expect to receive the following education and skills:

Education

  • BS or MS degree oriented
  • Data Science
  • Statistics
  • Business Analytics

Skills

  • Python or R
  • Data Analysis
  • Tableau
  • Jupyter Notebook
  • SQL
  • Regression
  • Model Building

Data scientists often have a background in business or data analytics before being able to utilize code, generally in Python or R, to automate projections using machine learning tools in those languages.

The path to becoming a data scientist or a machine learning scientist may differ as well. A data scientist, for example, may have started as a business analyst, statistician, data analyst, or business intelligence analyst before becoming a data scientist. It’s possible that a machine learning scientist will begin as a computer scientist, software engineer, robotics engineer, physicist, or engineer in general, and then advance to become a machine learning scientist.

Machine Learning Scientists

Machine learning scientists, on the other hand, are often more centered on the algorithms themselves, and also the software engineering around implementing the system. The word “research” is frequently used in the titles of machine learning scientists. This means you should spend more time understanding algorithms in general before developing a more straightforward method. Overall, these positions may be similar at different organizations, therefore it’s up to you to notice the differences every time you read a job description. The most important conclusion is that data science positions appear to be more common and stable, implying that their job descriptions do not change as much as machine learning scientists’, so take these abilities with a grain of sand.

The following are some of the variations in education and abilities required:

Education

  • D. degree oriented
  • Machine Learning
  • Computer Science
  • Robotics
  • Physics
  • Mathematics

Skills

  • Research-heavy
  • Signals & Distributed Systems
  • OpenCV
  • C++ or C
  • Quality Assurance
  • Automation
  • Model Deployment
  • Unix
  • Artificial Intelligence

Machine learning scientists typically demand additional software engineering skills, such as C++, and also more automation and deployment capabilities. In certain job descriptions, we’ll also notice that there’s a specialty, such as Physics or Robotics.

Conclusion

Data science is a wide, interdisciplinary discipline that seeks insights from massive volumes of data and computing power. One of the most interesting advances in modern data science is machine learning. Machine learning promotes machines to operate on their own from massive quantities of data. These technologies have several applications, but they are not without limitations. While data science is powerful, it can only be used to its full potential if you have highly skilled employees and high-quality data.

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