Lowe’s India Interview Experience for Data Scientist (On-Campus) 2023

Lowe’s India came for Campus Recruitment during placement season for the post of Data Scientist at IISc Bangalore. Following are the complete details about the interview process being conducted and the questions asked.

Eligibility: 60% without any backlogs

Skillsets Required: Predictive Modeling, Personalization, Recommendation algorithms, NLP and Text Mining.

Placement Process: It consisted of 5 rounds and all the rounds mentioned below were elimination rounds.

• CV Screening
• Online Assessment Test
• Technical Interview-1
• Technical Interview-2
• HR interview

Round 1: After CV screening approximately 90 students were shortlisted for the online assessment test

Round 2: Online assessment consisted of two ML and related coding questions. Nearly 20 Students were shortlisted for interview, after an online test.

• Question 1: To find the Joint, conditional, marginal probabilities and required probabilities based on the data given.
• Question2: It was a classification problem that needed to be coded using KNN from the scikit-learn library

Round 3: It was a technical interview, and the interviewer was someone with approximately 5 years of experience within the domain, the interview was to check basic skills and techniques within data science, following were the questions being asked:

1. Tell me your level of experience concerning Data Science
2. What is the complete process involved within Data Science starting from getting raw data to getting final results, observations and evaluation?
3. What are the different ways of cleaning the data or how do you get, model-ready data from raw one?
4. How do you select features?
5. How do you determine feature importance and ways of quantifying the importance of features?
6. What are the different evaluation metrics for regression and classification problems?
7. What does R-squared tell you in regression?
8. What is the range of values you get in R-squared, and what does it signify?
9. What is Interpretability? How do you determine whether a model is interpretable?
10. Tell me about interpretability in the context of Linear Regression and Decision Trees.
11. What is Hypothesis Testing?
12. Explain in layman’s terms, what does a p-value signify?
13. Why is there a notion that either we reject a hypothesis or fail to reject the hypothesis?
14. Tell me about your experience with NLP.
15. Tell me about some problems you have solved, and how did you solve them?
16. Any Questions?

Round 4: It was also a technical interview, and the interviewer was someone with approximately 18 years of experience within the domain, this tech interview was to learn about my projects, some situational design questions, and to get into depth of techniques I have used. Following were the questions being asked (Context: The project discussed was around the translation model I trained by fine-tuning the hugging-face pre-trained model):

1. Explain any of your favourite projects mentioned.
2. Which corpus did you use?
3. What was the size of the corpus you used?
4. Which model did you fintune?
5. What was the average sentence length for the input you used?
6. What evaluation metric do you use?
7. How much was the score of metric, and what it should be for good translations?
8. What are the other evaluation metrics that can be used?
9. Explain the Bleu Score, and how is it calculated.
10. What were the challenges you faced while making the project?
11. Gave me a Fraudulent transaction detection problem, and asked how to solve it.
12. How do you deal with an imbalanced dataset?
13. What must be the evaluation metric that must be used here?
14. How important are Precision and Recall here, can the F1 Score alone work here, based on some situations (Hint: Read about the F beta score)
15. What are the types of projects you want to work on, and problems you want to solve after joining the industry?
16. Any Questions?

Round 5: It was an HR round, it was more of a formal discussion after 20 minutes of QA. The following are the questions being asked:

1. Tell me about your experiences and what you have done after graduation.
2. Why were you rejected in Indian Army SSB interviews, and what have you learnt from that? (For 6 months I wasn’t doing anything else and was just preparing for SSB interviews only)
3. What do you feel are your weaknesses now, despite improving yourself after SSB interviews?
4. As you have worked in academia and taught students, how come you came to IISc for Master’s in AI?
5. What do you look for in a company you apply for?
6. Do you like to learn and work all by yourself, or do you take help, work in a group or team?
7. What type of people do you network with?
8. Any Questions?

Overall Experience: It was a great experience, the interviewers were quite supportive, There was an instance where one of the interviewers was not aware of something so instead of skipping or avoiding it, he was very keen to know about that. Online assessment was also more relevant and skill-based rather than simply programming questions like for most of the other companies.

Result: SELECTED

Short Interview Tips:

• Focus on building a better resume and try evaluating, based on the resume checker present online and asking other seniors to you.
• Be honest if you don’t know the answer.
• Be thorough with what you have done within your projects and internship
• No need to worry if you have gap years, you must be able to justify that confidently
• Always ask questions, for clarification if required and at the end of the interview as well.

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