Top 5 Characteristics of A Successful Data Scientist
Top 5 Characteristics of A Successful Data Scientist
The need for data scientists is considerable, and it appears that this skills deficit will last for the foreseeable future.

 According to an IBM report, there will be 2.7 million job openings for data-savvy workers in the US by 2022, up from the current 364,000. Furthermore, the study predicts that by 2022, there will be close to 700,000 openings for the rapidly expanding new data scientist, data developer, and data engineer positions.


Many businesses are forced to conduct extensive searches for competent applicants who can meet their technical requirements. But just because someone technically satisfies the requirements doesn't indicate they'll become a good team player. Hiring managers should look for other qualities in data scientists. 

HR managers should consider the following 5 qualities when recruiting a data scientist.


  1. Being able to solve problems

The ability to "think outside the box" in other areas of the job is just as important for data scientists as their ability to use data to solve problems. Data scientists might not have the right equipment and resources to finish a particular task because the industry is so young. With a Data Science Certification course in Delhi, you can master the skills and will be an expert data scientist. 


HR managers should search for applicants who can work around this issue and use available resources to finish data-related activities. As an alternative, data scientists who are familiar with the tools required for the work and who can request them are also excellent prospects. This will change when the market catches up with demand, but data scientists should be able to function without the latest technology and still do essential tasks.


  1. Data Storytelling Skills

In addition to breaking down and analyzing data, data scientists must also translate that data for non-technical team members. The ability to interpret data and, in essence, tell its story by describing how the team gathered the data, how they analyzed the results, and what they predict will occur in the future is a skill that qualified data scientists have.

  1. Curiosity

The field of data science is expanding impressively, and innovations will be made because those who work in it are driven to advance data applications. Candidates curious about how data is used would make good team members because they can assist the business in coming up with new uses for gathering big data.

Candidates who can function well in data science will almost always be in demand due to its rapid growth. Instead of simply employing anyone who satisfies the technical criteria, HR and executives should ensure candidates have these five talents to have a highly effective data team. In the end, this will produce a solid data science team that can advance the use of data by coming up with new, original solutions to issues.


As big data becomes increasingly important to a company's decision-making process, there will be a significant demand for data scientists who can successfully communicate a data story to the rest of the team.

  1. Collaborating well

Data scientists were polled by CrowdFlower, who discovered that they are all usually content with their jobs. According to the poll, cleaning and organizing data take up the most time for 60% of data scientists. The New York Times estimated this to be 80 percent a few years ago. This wouldn't be a problem, except that 57% of data scientists who were asked what aspect of their work they disliked the most said cleaning and organizing data.


  1. Analytical and Quantitative thinking

In order to determine what qualities make a good data scientist, the software company SAS polled data scientists. The most vital logic and analytical abilities group exhibited the most prevalent feature. These characteristics were exhibited by 41% of individuals polled, being the largest group.


Data scientists should have a technical bias, which means they should only use the data to support their claims rather than relying on intuition or emotion. Since others on the team might not have a technical background, this person needs to be able to communicate clearly and explain topics in a simple way.


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