Abhinesh Gupta, Machine Learning Engineer at EdGE Networks, talks about his exciting work and journey with the organisation so far.
1) With all that you’ve learnt, how are you applying your extensive knowledge at EdGE Networks?
Ans) During my tenure, here at EdGE Networks, the projects I have worked on so far surrounds:
- Demand Optimiser: Based on the Demand (requirement for a resource) description, provided alternatives that can speed up or have a higher chance of fulfilment.
- Attrition Analysis: With the given the supply data pertaining to employees, we are trained to provide insight into employees that are at a higher risk of leaving the company. This is done so that the most appropriate action can be taken pre-emptively.
- Skill Trend Analysis: With the given the Historical Demand Data, we provide a comparative view of how the demand creation looks like, with respect to the skills. This is further categorized, namely:
Trending skills: Skills which are going up.
De-Trending skills: Skills which are going down.
Stable skills: Skills whose trend is neither stable nor fluctuating.
New Trending Skills: Recently introduced skills with an elevated trend.
- Demand and Supply Forecasting: With the given the Historical Demand data and Transactional supply, we estimate the demands that are going to be in the system or the supplies that are going to be on bench in the next quarter.
- Demand and supply Descriptive Analytics: Given the Historical Demand data and Transactional supply, we provide a set of metrics that describe the current state of the system.
- Demand and Supply Prescriptive Analytics: Given the Historical Demand data and Transactional supply, we provide a set of metrics that provide a view of risky situations and bottlenecks within the system.
2) Tall us a little about your journey here at EdGE Networks.
Ans) I joined Edge Networks as an intern in the Analytics team. The uniqueness of the problems that I was supposed to solve motivated me to give my best. In my 2 plus years at Edge, I worked with a lot of brilliant minds and learned a lot from them.
3) What was the biggest challenge you had to overcome, with respect to Data Science?
Ans) While doing the attrition analysis, we had to learn patterns in human behaviour. The first challenge, with respect to the above, is that we had to record corporate pressures and challenges using numbers or text, which isn’t often easy.
And another challenge was calculating the rate of attrition. This is dependent on the mental state of the employee, which is hard represent in the form of numbers, with certainty.
In order to solve this problem, I had to depend on the data that is recorded by the client. Which means, most of the behavioural aspects of the data isn’t present. I then had to figure out ways to create intuitive features that, to some extent, can push an employee to get separated from the organisation.
Machine Learning Engineer