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 requirement, in their job’s advertisements.

Applicants on the other hand are similarly enrolling for Data Science courses and revamping their Data Science resumes, to meet this market gap. Our jobs platform alone has registered a growing demand of 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.

In this article, i have examined the difference between the different Data Science roles and the kind of course 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 Engineer

They are 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.

They make sure that the data is ready to be processed and analyzed.

Finally, they keep the ecosystem and the pipeline optimized and efficient and ensure that the data is available for use.

2.Data Architect

Data architecture maintains some common responsibilities as data engineering.They need to ensure that the data is well-formatted and accessible for data scientists and analysts and improve the data pipelines' performance.

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.

3. Data Scientist

This is the widely known title. 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.

4. Data Analyst

These is the second most known role as they 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.

Data analysts are often the ones in charge of preparing data for communication by preparing reports that effectively show the trends and insights gathered from their analysis.

5. Machine Learning Scientist

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

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.

6. Machine Learning Engineer

These professionals 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.

7. Business Intelligence Developer

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.

Business Intelligence 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.

8. Data Storyteller

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 use 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 compelling story that helps people better understand the data.

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 is in charge of monitoring the database, making sure it functions properly, keep track of the data flow, and create backups and recoveries.

10. Technology Specialized Roles

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.

These job roles apply to data scientists and analysis as well. For example, transportation Data Science specialist, or marketing storyteller, and so on.

Such job titles will be particular on the responsibilities it entails and will loosen the general scientist and engineers' workload.

What Next?

As the field of data science grows, the demand for data scientists grows and new job titles get created to meet the huge demand of the industry. Your goal should be 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 . It is time to Enroll for that Data Science Course .