PensionBox interview experience Real time questions & tips from candidates to crack your interview

Data Science Intern

PensionBox

2 rounds | 3 Coding problems

Ananya Goel

Fresher | Apr 2022

Selected

Interview preparation journey

Preparation

Duration: 2 months

Topics: OOPS, Machine learning algorithms, Data Preprocessing techniques, Natural Language Processing , SQL

Tip

Tip 1 : Have in-depth knowledge of the subject
Tip 2 : Do extra ML based projects
Tip 3 : Focus on research work

Application process

Where: Campus

Eligibility: No backlogs

Resume tip

Tip 1 : Focus on project work
Tip 2 : Should be brief

Interview rounds

Round

Hard

Video Call

Duration60 minutes

Interview date15 Apr 2022

Coding problem2

We could choose our slot for the interview. There were two moderators (including the CEO). Interviewer asked about the projects , research work and it was more like a situation was given and we had to solve it using ML .

1. Data Analysis

Since its a company managing Retirement Planning. I was asked what analysis could be done so that we could find insights on the user data.

Problem approach

Initially we focus on getting the data from cloud to our system.
Then we had to preprocess the data, since it would have a gazillion of unrequired fields
After missing values are removed, we consider only the fields that are required.
Then , we could apply Regression to predict the total asset under our platform.
Using regression , we could predict a how much close would he be to his goal, if he continues to save that much amount.
We could classify users based on location and activity status.
After this, just brief a little about the algorithms to apply and the interviewer was impressed.

2. Technical Questions

I was asked about my research work.

Projects Discussion.

Problem approach

I explained them about my research paper in detail giving examples.
About projects done in previous internships, I gave an overview about them.
They asked me to explain more about the logic of the project
After that I was asked to share my Github and Linkedin Profile

Round

Medium

Face to Face

Duration40 minutes

Interview date24 Apr 2022

Coding problem1

Face to face interview with the Founder

1. Machine Learning

I was asked how would I create a data pipeline from scratch and what factors should I consider?

Problem approach

I told them we could get user data such as their DOB , Gender, retirement status, etc and we could focus on people in the age group of 27- 35 as these people ae the sweet spot who would want to plan their retirement and we wouldn't have to put so much effort into explaining why retirement planning is important.