I recently had the opportunity to interview at Swiggy for the role of Data Scientist 1, and here’s how the process unfolded:
Step 1: CV Shortlisting
I leveraged a referral from a friend working at Swiggy, which helped get my CV noticed. My profile was shortlisted based on my previous work in Data Science, particularly in Gradient Boosting Trees (GBT) and Recommender Systems.
Round 1: Interview with the Data Science Team Lead
Swiggy follows a top-down approach in its interview process. My first interaction was with the Data Science Team Lead. Key highlights of this round:
1. Discussion on Current Work: Focus on my ongoing projects and their business impact.
2. Technical Questions:
- Markov Chain Probability concepts.
- GBT (Gradient Boosting Trees): Detailed questions about residual calculations in GBT classifiers, hyperparameters, etc.
- AUC-ROC Curve: Understanding and evaluating models using it.
- Questions on L2 Norm and Cosine Similarity.
Round 2: CV Review and Case Study
The second round was an in-depth CV review, covering the models and evaluation metrics I mentioned in my resume.
- Case Study: I was tasked with solving a recommendation system problem focused on ad matching and optimizing user engagement.
Offer
Although some of my friends faced a third interview round, I received an offer after Round 2.
This was my interview experience with Swiggy, and I hope it provides useful insights for anyone aspiring for similar roles.
