101 guide to OYO Rooms Data Scientist Interview 2022

OYO room is one of the biggest hospitality service and affordable network chain of hotels in India. Due to it’s heavy customer based model of operations, it has a massive amount of data for each booking that is made on a daily basis. Managing sensitive data in a secure manner is a top priority for OYO. 

For these reasons, OYO requires efficient Data Scientists on their team. Let’s explore how the interview for a Data Scientist role takes place.

What is the Data Scientist interview process at OYO?

It is a very streamlined process which includes only 3 rounds which are a written test, a technical round and an interview with the HR.

Data Science interview rounds at OYO

Round-1 Written Test

This round consists of a programming based assignment, which may be online or offline. It is a simple test to evaluate your coding skills. Since this is the initial stage, the assignment is fairly easy but one should still be well prepared with data structures and algorithms.

A good example of this can be:

Design a stack-like data structure to push elements to the stack and pop the most frequent element from the stack.

Implement the FreqStack class:

• FreqStack() constructs an empty frequency stack.
• void push(int val) pushes an integer val onto the top of the stack.
• int pop() removes and returns the most frequent element in the stack.
  • If there is a tie for the most frequent element, the element closest to the stack’s top is removed and returned.

Pro Tip: Be clear how you will proceed before you start writing your code.

Round-2 Technical Round

The candidates who clear the written round are called for Technical Interview. To clear this round one should be clear with their basics. You should be prepared with DBMS, Data structures and Algorithms, Operating Systems. 

You should also be well prepared with system design problems. Students will be expected to write more codes in this round. They frequently ask questions about your experience with previous projects and the approach you took to tackle them. 

Pro Tip: Check your code for errors and bugs before you present it to the interviewer, it’s never a good idea to present a code with errors and bugs because they will always find the smallest bugs in your code.

You may be asked puzzle based questions in this round as well. An example of this would be:

How can you represent days of month using two 6 sided dice? You can write one number on each face of the dice from 0 to 9 and you have to represent days from 1 to 31, for example for 1, one dice should show 0 and another should show 1, similarly for 29 one dice should show 2 and another should show 9. 

Round- 3 HR Round

This round will mainly be concerned with getting to know about you as an individual and why you would be a great fit at OYO. You will be asked general questions about your goals, aspirations and resume related questions that will round up the interview questions. 

Questions like: 

• What are your strengths and weaknesses?
• Why OYO Rooms?
• Where do you see yourself in five years from now?

20 questions that are frequently asked at an OYO interview:

EASY

1. How will you treat missing values during data analysis?
2. What are the differences between univariate, bivariate and multivariate analysis?
3. What is the difference between the Test set and validation set?
4. What is the importance of dimensionality reduction?

INTERMEDIATE

1. What are the available feature selection methods for selecting the right variables for building efficient predictive models?
2. Will treating categorical variables as continuous variables result in a better predictive model?
3. What does the ROC Curve represent and how to create it?
4. What do you understand by a kernel trick?
5. Differentiate between box plot and histogram.
6. How will you balance/correct imbalanced data?
7. What are some examples when false positive has proven important than false negative?
8. Give one example where both false positives and false negatives are important equally?
9. What are various assumptions used in linear regression? What would happen if they are violated?
10. How do you identify if a coin is biased?

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HARD

1. What is better- random forest or multiple decision trees?
2. Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. Evaluate the probability of finding at least one shooting star in a one-hour duration?
3. Toss the selected coin 10 times from a jar of 1000 coins. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Estimate the probability of getting a head in the next coin toss.
4. Is it good to do dimensionality reduction before fitting a Support Vector Model?
5. How is feature selection performed using the regularization method?
6. How is the grid search parameter different from the random search tuning strategy?

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