Small and medium-sized businesses typically have one or two data scientists. While the big tech companies have more data scientists who are broken down into categories based on the tasks they’re best at. So. In this article, I’ll walk you through the types of data scientists you’ll find in big tech companies.
Types of Data Scientists
From analyzing data to creating a data product, it’s all about data science. Now, that doesn’t mean you have to know everything. You must know each area and be specialized in a particular area. Because the work of a data scientist depends on the field in which he/she works.
There are therefore 5 types of Data Scientists who are classified according to their work. The work is related to data, but the difference is in the field in which they work and the experience they have. Here are the 5 types of data scientists:
- Research Scientist
- Applied Scientist
- Machine Learning Engineer
- Data Engineer
- Data Scientist
Now let’s understand their job roles one by one.
Research Scientists are research experts. They don’t build models or pipelines. They are experts who are best at framing experiments, testing hypotheses, proving whether a junior data scientist is true or not. A data scientist with experience in both management and analysis can become a research scientist.
The applied scientist is on the same level as a Research Scientist, but they are also professionals in applying their domain knowledge and implementing it to provide solutions. It can be said that a scientific researcher focuses more on scientific discoveries while the applied scientist focuses on real-world applications.
Machine Learning Engineer
Machine learning engineers are professionals who can do the work of both software engineers and analysts. The main goal of a machine learning engineer is to feed data into models prepared by a Data Scientist. They are also responsible for creating machine learning models to control robots and other smart devices.
Data engineers are professionals who prepare big data for a data scientist. Think of a data engineer as a software engineer who designs, builds, and integrates data from various data sources within an organization and presents it as big data. Hadoop and MySql are essential skills for a data engineer. We can say that the raw data that a Data Scientist obtains to derive information is processed by a Data Engineer.
Now, this is a generalist Data Scientist who is responsible for analyzing and building machine learning models. The main goal of a Data Scientist is to analyze and prepare machine learning models capable of solving critical business problems. We can say that a data scientist is someone who turns a company’s big data into actionable information.
All of the Data Scientist categories I have featured in this article are based on the positions you hold in large tech companies. The work of a Data Scientist is divided into sub-categories so that each professional can focus on what they are doing. But in small to midsize tech companies, you might only see one Data Scientist doing all the work, from data engineering to machine learning engineering. These companies generally do not have research and applied scientists.
I hope you liked this article on the types of Data Scientists. Feel free to ask your valuable questions in the comments section below.