Commonwealth Bank of Australia

Role: Data Scientist

College: Not Specified (On-Campus Placements)

Interview Date: January 2025

Interview Type: On-Campus, Full-Time

Result: Not Specified

Difficulty: Medium to Hard (implies thorough preparation needed)

Rounds: 3 rounds

Topics Asked: ML Concepts, Statistics, Coding Fundamentals, Resume Projects, L1/L2 Regularization, CNN, Bayes Rule, Case Study, Behavioral, HR.

Experience:

This candidate interviewed for a Data Scientist role at CBA as part of on-campus placements. The role involved leveraging advanced machine learning and data analytics techniques.

Round 1: Online Assessment

Not explicitly detailed in the provided snippet, but mentioned as part of the overall process.

Round 2: Technical Interview

• ML Models: Working principles, advantages/disadvantages, and suitable datasets for models like Decision Tree, Random Forest, and XGBoost.
• L1 vs L2 Regularization: Differences and applications.
• CNN Structure: Explanation of each step, methods to reduce overfitting, and reasoning for each action.
• Bayes Rule: A simple application-based question.
• Case Study: Predict customer behavior for a new product launch. This involved:
• Choosing the best ML model.
• Explaining EDA, data cleaning, and feature engineering processes.

The interviewer also delved into resume details with real-world examples.

Round 3: HR Interview

• General queries about the candidate's day and energy levels.
• Discussion on previous internships and co-curricular activities.
• Debate: "GenAI is a fad" (opposing views).
• Situational questions, e.g., handling disagreements with managers in prior roles.
• Random HR questions.

Key takeaways provided by the candidate included being thorough with ML concepts, statistics, coding fundamentals, defending resume details, maintaining a positive mindset, and clear communication in HR rounds.