Different job roles in Data Science

job roles in Data Science

Data Science is a growing domain and has many job opportunities. Companies are looking for Data Scientists with technical knowledge and communication skills. Despite the high number of job opportunities the field offers, there is still a skill gap in this domain. Experts’ opinion that the lack of job-ready skills in the fresh graduates and professionals is the culprit.

It has become vital for both professionals and freshers to upskill themselves to bag a job. You can join the top-rated Data Science training program offered by 360DigiTMG to upgrade your skills. Now your next tedious task is choosing a role you want to start and grow. Data Science is a vast domain with multiple job roles. You can always choose a job role depending on your educational background, work experience, or even out of interest.

There is always confusion among aspiring professionals about what suits them best according to their skillset. In this article, we will decipher the different roles and skills needed for it. There are many job titles but, we will be discussing the top ten only. Data Science is dynamic and is growing rapidly, these titles might change in the future.

Data Scientist

Data Scientist is the most popular role in this domain. A Data Scientist deals with all aspects of data, from the business side to data collecting and analyzing with visualizing and presenting. Being a Data Scientist means knowing a bit of everything, every step of the project this makes them give insights and come up with the best solutions and can reveal patterns and trends for the project. A Data Scientist is also responsible for developing new algorithms and approaches. In many multinational companies, Data Scientists will be heading a team which makes them overlook a project and guide the team.

Data Analyst

Data Analyst is the second most famous role, it is often a misconception that Data Scientist and Analyst are same roles but a Data Analyst specifically works on Data Analytics. They are responsible for visualizing, transforming, and manipulating data. They also handle tracking web analytics and A/B analysis. A Data Analyst is responsible for visualization, they prepare data for communication and prepare reports to highlight and trends and insights from their analysis. Click here to learn data science course in hyderabad

Data Engineer

A Data Engineer’s job profile is different compared to a Data Scientist and Analyst, they design, build and maintain data pipelines. They prepare ecosystems for data scientists to work on algorithms. They classify data batch-wise and process and analyze it. By doing all this a Data Engineer prepares data for a Data Scientist and Data Analyst to use.

Data Architect

A Data Architect has a similar responsibility as a Data Engineer. The main task is to keep the data ready and formatted for Data Analysts and Scientists. They improve data pipelines developed by Data Engineers. They also need to design and maintain new database systems for both functionality and administrative perspectives for specific business models and job requirements. They can decide who can view use and alter data.

Data Storyteller

It is one of the new job roles that have emerged recently and is a significant and creative job. No, it is not similar to Data Visualization; there are many differences between them. Data Storytelling not only visualizes the data and makes reports and stats but also narrates and describes the uses of data. A Data Storyteller takes data, simplifies it and decipher it and understands its patterns and trends, and builds a pitch or a story that can help people understand data better.

Machine Learning Scientist

Machine Learning Scientist requires to do research and come up with solutions, algorithms, and insights for optimal use of data. An ML Scientist comes up with data manipulating approaches and plans new algorithms. They mostly work in Research and Development. An ML Scientist is also called a Research Scientist or Research Engineer.

Machine Learning Engineer

 Machine Learning Engineer is the second in demand in the market after Data Scientist. They need to have knowledge about various ML algorithms like clustering, categorization, and classification and should be up to date with the latest technological advancements in the field. An ML Engineer should have a good grip on statistics, programming, and basic software engineering. They also need to design, build Machine Learning Systems and run tests such as A/B tests-and monitor how machines are performing.

Database Administrator

Many companies nowadays are getting their database designed by a third party. After buying a database from designers it is maintained by the company’s Database Administrator. Businesses are hiring Database Administrators to monitor and manage the database, keeping the track of data, and create backups and recovery.  They are responsible for giving permissions to different employees depending on the level of clearance.

Conclusion:

Data Science is a new field that is still growing and, the job roles that are there today may expand in the coming future. There is always a chance for the emergence of new roles with changing responsibilities. With the rate of domain growth happening it does not seem to slow down anytime soon. Click here to learn data science course in bangalore.

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