qscore-logo Q Score

What is Q-Score?

Q-Score is a grade given to each Analyst on the Econolytics platform that gives weightage to both the ‘Intelligence Quotient’ as well as the ‘Emotional Quotient’ of an Analyst. It takes into consideration not only the profile that the Analysts have created themselves but also on challenger questions and tests that are constantly posted on the Analyst’s Dashboard. Therefore, the score is a dynamic field that gives Analysts the opportunity to constantly increase their ranking on the system.

What are the parameters taken into consideration for a Q-Score?

There is a glut of Data Analysts in the market and it becomes difficult for companies to assess which analyst would be able to deliver customized projects for them. From a distance, all the analysts may even appear to be clones of each other; especially for small/medium companies who donot have much experience in hiring Analytics talent.
The concept of the Q-Score is to help companies make the right decision on who they want to hire on projects, not only based on standard parameters such as years of experience and industry domain, but also what are the key aspects of an Analyst’s personality that the project will actually require.

Broadly speaking, the Q-Score takes into Account the following parameters:

1 Intelligence Quotient
i Mathematical bent of mind
ii Logical Deductions
iii Programming Skills
iv Domain Knowledge
2 Emotional Quotient
i Commitment & Dependability
ii Team oriented
iii Leadership skills

The actual weightage and algorithm for arriving at the final score is an internal rating system, based on the perceived performance on deliverables by each Analyst.

How do we collect Data round Analyst performance?

Currently, we have worked in partnership with various experts across the board to understand how to evaluate the parameters laid out in the above section. Example: We have worked with a Data Scientist from Australia with more than 10 years of experience to help us design Challenger questions specifically on machine learning algorithms. On the other hand, we are working with a Dr. in Psychology to develop Psychometric tests to evaluate a candidate on the Emotional Quotient. We have then worked with HR consultants to understand the intuitive weightage to be placed for each of these parameters. Needless to say, this scoring algorithm will be simulated & validated on recent data to run further iterations and improvise accordingly.