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What You Should Know About the Different Data Science Job Titles

The rising popularity of Data Science jobs and the ever-growing demand by organizations for talent possessing Data Science as a skillset begs for the constant need to retool your skills to match the relevant gap in the market. To be able to match the revolutionary changes in technology, Organizations are now including Data Science soft skills requirements, in their job advertisements.

Applicants on the other hand are similarly enrolling for Business Analytics & Data Science courses and revamping their Data Science resumes, to meet this market gap. Our jobs platform alone has registered a growing demand for Data Science related jobs – and while this is good news, going for the perfect Data Science job pick can be baffling, considering the skill itself emerged as a result of the changes in tech and was therefore not taught in most institutions of higher learning as a generic job to apply for.

Predictive Analytics Lab CEO Timothy (Front Left in Maroon sweater) with the Management of Moi University after signing MOU on Mentorship for University Students Program

In this article, we explain the difference between the variant Data Science roles and the kind, of programs that can get you that slot. The banter however is that – the job titles are not fixed and may change in the future, and some of the roles mentioned below may overlap and have more or fewer responsibilities based on the company hiring. However, this article should help you explore the different data science roles and eliminate confusion.

1. Data Analyst

The second most known role is that of a data analyst. While the roles of a data scientist and a data analyst overlap in different organizations, data analysts are responsible for different tasks such as visualizing, transforming and manipulating the data. Sometimes they are also responsible for web analytics tracking and A/B testing analysis.

As data analysts are mostly placed in charge of visualization, they are often the ones in charge of preparing the data for communication by preparing reports that effectively show the trends and insights gathered from their analysis.

Read more: The evolution of Data Visualization tools  

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2. Data Scientist

Data Scientist falls under one of the most and widely known titles. Becoming a data scientist entails dealing with all aspects of an organization’s project. From data collection to analyzing and finally to visualizing and presenting – these are the day-to-day tasks of a data scientist.

As a data scientist, you can best offer insights on valuable solutions an organization can take, and also uncover patterns and trends observed over time. Moreover, data scientists are placed in charge of the research and development of new algorithms. Often, in big companies, team leaders in charge of people with specialized skills are data scientists; their skill set allows them to overlook a project and guide them from start to finish.

3. Data Storyteller

This is probably the newest job role in this list and yet quite significant and creative. 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 using it to express it.

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 create a compelling story that helps people better understand the data.

4. Data Engineer

Borrowed from the generic engineering term; data engineers are the personnel responsible for designing, building, and maintaining data pipelines. They test ecosystems for the businesses and prepare them for data scientists to run their algorithms.

They 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.

Finally, they are the same people that keep the ecosystem and the pipeline optimized and efficient and ensure that the data is available for data scientists and analysts to use.

Predictive Analytics Lab Trainer Robert, Explains to the class a Concept on Hadoop and Mapr during a virtual Data Engineering class

5. Data Architect

Data architecture maintains some common responsibilities as data engineering. In both, the two professionals need to ensure that the data is well-formatted and accessible for data scientists and analysts and improve the data pipelines’ performance.

Additionally, data architects are placed in charge of designing and creating new database systems that match the requirements of a specific business model and job requirements. Their task is 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.

6. Machine Learning Scientist

A machine learning scientist researches new data manipulating approaches and designs new algorithms to be used. They are often a part of the R&D department, and their work usually leads to research papers. Their work is closer to academia yet in an industry setting.

Job titles that can be used to describe machine learning scientists are Research scientists or Research engineers.

Modules to be covered in our Business Analytics and Data Science Level 1 Program

7. Machine Learning Engineer

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 also be up-to-date with the latest research advances in the field.

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. In addition to designing and building machine learning systems, machine learning engineers need to run tests — such as A/B tests — and monitor the different systems’ performance and functionality.

8. Business Intelligence Developer

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. 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.

BI developers’ 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.

Student at Predictive Lab shares his screen during a class presentation on using powerBI for data Visualization.

9. Database Administrator

Sometimes the team designing the database are different from the ones using it. Currently, many companies can design a database system based on specific business requirements. However, the database’s managing is done by the company buying the database or asking for the design.

In such cases, each company hires professionals to be in charge of managing the database system. A database administrator will be in charge of monitoring the database, making sure it functions properly, keeping track of the data flow, and creating backups and recoveries.

10. Technology Specialized Roles

Data Science is still a developing field; as it grows, more specific technologies will emerge, such as Artificial Intelligence or specific Machine Learning 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.

These job titles apply to data scientists and analysis as well. For example, transportation Data Science specialists, or marketing storytellers, and so on. Such titles will be particular on the responsibilities it entails and will loosen the general scientist and engineers’ workload.

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Final comments

Most often, the term “scientist” in a job title indicates this role requires doing research and coming up with new algorithms and insights. And as the field of data science grows, the demand for data scientists grows as well and new job titles get created to meet the huge demand of the industry.

The goal is to place yourself in an environment where you can constantly learn and keep reskilling and upskilling to remain marketable and a highly sort after talent in the dynamic job space. Feel free to sign up for our 4IR Club that will offer you a never-ending sea to keep learning the industrial trends.

What are you waiting for? Enroll in Data Science Courses to start or progress your journey in data science. To get constant updates and mentorship in the field, follow our Facebook, Twitter, Linked In, and Instagram pages or subscribe to our newsletter.

Predictive Analytics Lab has a platform for jobs, consulting gigs, and tenders in 4th Industrial Revolution-related technologies and careers. Set up a profile here to receive notifications on emerging opportunities.

Last but not least drop us an email at or call +254725349693 for any inquiries.


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