Whenever we talk about disruption, we talk about the emerging role of modern day data scientist. But who are they?

Simply put, he/she is a person responsible for collecting data, analysing data and interpreting them to identify how businesses can improve operations and gain a competitive edge. For this, they leverage on the formidable analytical, programming and statistical skills, industry knowledge, and conceptual understanding to unearth hidden insights to produce meaningful solutions to solve complex business challenges.

In the 21st century, the job of a Data Scientist is said to be the sexiest job. But, what makes it so cool?

  • India is the second analytics hub in the world after the U.S.
  • The jump in job posts is across all industries including banking, energy and utilities, retail, airlines and healthcare.
  • Demand has exceeded the supply as there is a huge uptick in such roles.
  • The entry-level salary of these sought-after scientists is more than Rs. 6,00,000 p.a.

data scientist

So, what it needs to be a good Modern Data Scientist?

Data Scientists aren’t born, they are made. And these are a few skills and traits that make them perfect fit for their job:

  • A problem solver at heart, who addresses real-world problems in an efficient way
  • A math whiz, who possesses strong skills to solve complex calculations
  • An insight juggler, who waters the roots of insights and applies them to work
  • A quant-aficionado, who is empowered with programming and analytical skills
  • Storyteller, who translates insights into a story to communicate to his peers

The mix of personality traits, analytical skills and experience are required for the role to be a qualified data scientist. Well, it is so because they are needed to take on some serious responsibilities.

Gathering and analysing data, using various analytical and reporting tools, determining trends and relationships in data sets are just a few of their basic responsibilities. Their typical work is to mine big data, which can be used as information to predict customer behaviour and identify business opportunities and risks.

Developing statistical learning models for analysing data in their work. To do so, they must have the ability to create and assess complex predictive models and experience in using statistical tools.

The varied roles in the data science industry:

With the emergence of AI in all industries, the demand for skills sets such as data analytics, data science, data engineer and machine learning is swelling at a lightning rate. Here are various roles in the data science industry:

  • Data Scientist: They are big data wranglers. They use their formidable analytical, skills, programming tools and statistical techniques to clean, organize and analyse gargantuan messy data to share actionable business insights with his peers. These roles are highly in demand in companies such as Google, Facebook and Amazon.
  • Data Analyst: Some might confuse Data Scientists with Data Analysts, but they are not the same. Though there is an overlap in many of the skills, they are significantly different. A data analyst is proficient in languages such as R, Python and SQL. The key responsibilities include collecting, processing and performing data analysis using statistical methods.
  • Data Engineer: He normally has a background in engineering and plays around with databases and large-scale processing systems.
  • Machine Learning Engineer: As a machine learning engineer, your end output is a software that can run independently with minimal human intervention / supervision. They are computer programmers and focus on programming machines to perform a specific list of tasks. For example, a machine learning engineer may work on the self-driving car, robot, etc.