Different Job roles in Data Science and Analytics Industry

Posted on Posted in Data Science, Machine Learning

Data Engineer

A Data Engineer is a person, fully equipped with knowledge of hardware, databases, data processing at scale and computer engineering and who can build data infrastructure, manage data storage and use and Implement production tools. He can be from any background like: Computer Science and Engineering, Information Technology, Computer Science.

Data Scientist:

A data scientist is responsible for pulling and cleaning data, designing experiments, analyzing data and communicating result. He should have stronger statistics and presentation skills than a data analyst and data engineer. A data scientist would have strong skills of Inferential Statistics, Machine Learning, Data Analysis, Data Communication.

“There’s a joke running around on Twitter that the definition of a data scientist is ‘a data analyst who lives in California,” — Malcolm Chisholm

 

 

Data Science Manager:

A Data Science Manager is a person who builds a data team, manages the whole data science process, set goals and priorities and interact with other groups and higher management. He should be strong knowledge of software and hardware, knowledge of roles, strong communication and he knows what can and can’t be achieved.  A Data Manger can be any background like: Data science plus management skills or Data engineering plus management skills  or Management skills plus got certain training in data science.

Data Architect:

A Data Architect understand all the sources of data and responsible for integrating, centralizing and maintaining all the data. He has strong knowledge of how the data relates to the current operations and the effects that any future process changes will have on the use of data in the organization. His role may include may include things like designing relational databases, developing strategies for data acquisitions, archive recovery, and implementation of a database, cleaning and maintaining the database by removing and deleting old data etc.

Data Analyst:

Data analysts need to have a good understanding of programming, statistics, machine learning, data munging, and data visualization. He may not have the mathematical or research background to invent new algorithms, but they have a strong understanding of how to use existing tools to solve problems and get new useful insights from data.

Business Analyst:

Business Analyst performs the task of understanding business change needs, assessing the business impact of those changes, capturing, analyzing and documenting requirements and supporting the communication and delivery of requirements with relevant stakeholders. The business analyst role is often seen as a communication bridge between IT and the business stakeholders. Business analysts must be great verbal and written communicators, tactful diplomats, problem solvers, thinkers and analyzers – with the ability to engage with stakeholders to understand and respond to their needs in rapidly changing business environments.

Software Engineer:

Why we need software engineers in data science team? Software engineers are also needed in data science team because Software is the generalization of a specific aspect of a data analysis. If specific parts of a data analysis require implementing or applying a number of procedures or tools together then we need to build a piece of software to reduce the repeated work. In a sentence we can say that Software engineering is used to generalize data analyses into software so that they can be applied in different situations

  • Suman

    Nice Post 🙂

  • Gunjan Singh

    Really like it dude! Good post keep it up

  • sid

    nice

  • Vivid

    I was wondering the same question and somehow found this post in Bing Search. I think the website is crested very recently. Anyways all the job roles are captured properly and I got a clear idea!!!

  • Nusrat Khatun

    Good Article. absolutely amazing!

  • Bob

    Thanks for sharing this nice information.

  • FrankGT

    So, the data analyst should learn a few new tricks and call himself data scientist