Solytics Partners Interview Experience | Data Scientist + GenAI | Campus Placement

Hi everyone!

I recently received my official offer letter from Solytics Partners for a Data Scientist role with GenAI responsibilities through campus placement. I'm sharing my interview experience to help students preparing for similar roles.

Interview mode: Virtual
Rounds: L1 Technical -> L2 Technical -> HR

Round 1: L1 Technical (30 minutes)

The interview started with "Tell me about yourself" and a discussion about one of my AI/ML projects.

Topics covered:

• Supervised vs. Unsupervised Learning, and ML Algorithms.
• Linear Regression, KNN, Hierarchical Clustering, and SVM
• Why Logistic Regression is used for classification
• Accuracy, Precision, Recall, F1-score, and Correlation Matrix
• About pandas, numpy and matplotlib
• Missing-value handling
• Medium-level coding question

After around 2-3 days, I received the email confirming my selection for L2.

Round 2: L2 Technical (30 minutes)

This round focused more on projects, implementation, and practical problem-solving.

Topics covered:

• Internship and project discussion.
• RAG architecture and implementation.
• Questions from CNN and about its architecture.
• Questions from NLP.
• Questions from Deep Learning and Gen AI.
• Embeddings, vectors, and Vector DB vs. traditional DB
• Imbalanced classes, and Bagging vs. Boosting
• NumPy and pandas

Coding question: Given two pandas DataFrames, find the top 10 salaries using pandas, not SQL. Also asked another similar question from the pandas.

I was also asked a scenario-based question involving three APIs, where the output of one API becomes the input to the next. We discussed possible approaches such as XGBoost, an LLM, and n8n. The key learning here is to understand the requirements first: sequential API integration is generally a workflow orchestration problem, while ML or LLMs are useful when the individual steps require them.

After another 2-3 days, I received the email for the HR round.

Round 3: HR Interview (15 minutes)

The HR round focused on communication, motivation, and workplace scenarios.

Questions included:

• Tell me about yourself and your internship experience.
• Why do you want to pursue Data Science?
• How would you manage additional work if a teammate is on leave?
• How would you learn a new tool within one week?
• Questions about location, role, and working hours.