Data engineers are responsible for designing, building, and maintaining data pipelines. They need to test ecosystems for

The variety of data science0related roles often means that their respective responsibilities overlap a little — and sometimes a lot —causing confusion for applicants trying to get their dream job.

Business Intelligence developers — also called BI developers — are in charge of designing and developing strategies that allow business users to find the information they need to make decisions quickly and efficiently

Machine learning engineers are very on-demand today. They need to be very familiar with the various machine learning algorithms like clustering, categorization, and classification and are up-to-date with the latest research advances in the field.

It lays right in the middle between pure, raw data and human communication. A data storyteller needs to take on some data, simplify it, focus it on a specific aspect, analyze its behavior, and use his insights to create a compelling story that helps people better understand the data.

Often, data storytelling is confused with data visualization. Although they do share some commonalities, there is a distinct difference between them. Data storytelling is not just about visualizing the data and making reports and stats; rather, it is about finding the narrative that best describes the data and uses it to express it.

In this article, I went through 10 or the commonly used data science roles’ titles and a brief explanation of the responsibilities expected for each of them. Hopefully, this list will help you get the correct job for your skillset or at least give you an idea of what’s available.

Data engineers also work on batch processing of collected data and match its format to the stored data. In short, they make sure that the data is ready to be processed and analyzed.

In such cases, each company hires a person — or several1to be in charge of managing the database system. A database administrator will be in charge of monitoring the database, making sure it functions properly, keep track of the data follow, and create backups and recoveries.

BI developer’s work is mostly business-oriented; that’s why they need to have at least a basic understanding of the fundamentals of business models and how they are implemented.

Data science is still a developing field; as it grows, more specific technologies will emerge, such as AI or specific ML algorithms. When the field develops in that manner, new specialized job roles will be created—for example, AI specialists, Deep Learning specialists, NLP specialists, etc.

To perform their job properly, machine learning engineers need to have strong statistics and programming skills in addition to some knowledge of the fundamentals of software engineering.

Aside from that, they also need to be very comfortable using new BI tools or designing custom ones that provide analytics and business insights to understand their systems better.

They need to maintain these database systems, both from the functionality perspective and the administrative one. So, they need to keep track of the data and decide who can view, use, and manipulate different sections of the data.

A machine learning scientist researches new data manipulating approaches and design new algorithms to be used. They are often a part of the R