5 Different Data Science Roles that you Need to be Aware of

Data science is a multidisciplinary field that combines the forces of statistics, mathematics, and computer science. Understanding of statistical models, level of comfort with multivariate calculus and linear algebra, and fluency in programming languages, all contribute to the becoming of a data scientist. However, when we talk about data science or the job roles in data science, we often overlook the fact that it is not just about data scientists. There are a bunch of different roles that fall under the umbrella of data science and they are quite as important. We will talk about five different job roles in the field of analytics and data science which should give you a wider spectrum of choices. Data science modeler This role requires you to be a problem solver. You find the solution and leave the execution of that solution to others. Let me explain. As a data science modeler, you work in a business environment where the stakeholders come up with a problem. You speak to them, understand the problem, and then try to further qualify and quantify that problem until you can come up with a data-centric approach towards that problem. Your next job is to ideate an analytical model that addresses the problem at hand. You prepare the data for the procedure, design a bunch of algorithms, select the one that you think will get the job done. The machine learning engineers take it from there and give life to the model that you may have designed on your Jupyter notebook. As a data science modeler, you have to be at the top of your game in terms of statistical analysis. You have to get the math right if you want it to work. You need an in-depth understanding of the domain you are working in. You need stellar communication skills. Great coding skills are a plus, but you can make do with less than that. Machine learning engineer This role completes the previous one. The models ideated by the data science modeler have to be made actionable and the machine learning engineers do that. Machine learning engineers work with object-oriented programming languages like Python. They train the algorithms with suitable data and deploy them. The importance of this role is ever increasing. This is the role that most software engineers assume when they start working in data science. Data analyst This role can be looked at as a more commercialized version of the data scientist role where the professional is more concerned with saving the company’s money and other resources by finding out the most cost-effective model to solve a problem rather than creating the most accurate model. The data analyst works closely with stakeholders; understands their requirements; plans a data strategy that may get him close to solving that problem. This role involves data mining skills, database management skills, and statistical analysis skills, reporting, and visualization skills. Data architect Neither a data scientist nor a data analyst can work without stable and secure data pipelines. The data architect is responsible for creating databases that are very secure yet easily accessible provided one has the right credentials. The architect needs also to make sure that database maintenance is simple and that it is scalable. Business analyst The interdependence of data analytics and enterprises is prominently accentuated in the role of a business analyst. The business analyst needs to combine analytics skills and management skills to perform in a very demanding environment. The BA professional is responsible for handling stakeholders’ requirements; using data to optimize business processes to meet those requirements effectively; maintaining harmony between different sections of the company. They need a balanced skill set that combines technical skills and soft skills. They should be able to work with databases, be conversant with SQL, be skilled in handling spreadsheet tools like Excel, possess Tableau skills, know a programming language, have an understanding of statistical models.

Being a part of the data science industry is a boon as well as a challenge. It requires you to be on your toes so that you can adapt yourself to its fluidity and dynamism. At the same time, it keeps you ahead of most other technical workers in terms of being future-ready. Bangalore has grown to become a hub for data-centric enterprises and the quality of data science training in Bangalore has been up to the task of providing the industry with skilled professionals.  

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