What does Nupoor Gupta, Data Scientist at EdGE Networks, have to say about her journey so far?
1) What prompted you to first join EdGE Networks?
Ans) Since EdGE Networks worked in a domain that was relatively new, it provided a platform for new employees like me to explore and enhance our skills by gaining constant knowledge regarding the technology.
2) How do you think the Data Science team has helped simplify Talent Decisioning?
Ans) The Data Science team has helped simplify Talent Decisioning through several methods. One of which was the proper analysis of data and constant creation of analytical reports, which contained beneficial insights about a candidate’s profile, demand-to-supply ratio, conversion rate of supplies from bench to demand fulfilment for our HIREalchemy platform. Through this, our brain (EdGE Graph) is now capable of delivering efficient search and match functions, Talent Transformation and career insights.
3) What are the key challenges faced by the Data Science team on a regular basis?
Ans) There a various and diverse challenges that a Data Science team faces daily. However, the most pressing area is that of ‘Data’. Data from different clients come in different forms, hence we have to normalize it before utilising it. This is often time consuming.
4) What keeps you firmly rooted in your place, working with EdGE Networks in the HR Tech space?
Ans) Our Data Science team works hard, but we know how to have our share of fun. Serious work is often softened with light moments amongst the team members. Also, we are currently working on a problem statement which is touted to be one of the toughest recommendation engines ever, and this excites me. We are experimenting and using latest algorithms, which is important to gain knowledge in our field.
5) Accuracy represents how efficiently our algorithm works and functions. During your tenure here with EdGE Networks, how have you managed to improve the accuracy of its algorithm?
Ans) We are currently working on our Search and Match version 2.0. Hence, we are working hard to improve our algorithms, whilst keeping in the mind all the errors that prevailed in the initial version. In order to execute this task successfully, we are scrutinizing client-based analytical reports, cleaning the data properly before using it, creating additional features in the data by employing feature engineering, as well as constant discussions with recruiters and hiring managers to gain information and hiring insights.