Anz Bank

Role: Graduate Software Engineer (On-Campus)

College: Not Specified (On-Campus)

Interview Date: Not Specified (Published Jan 9, 2025)

Interview Type: On-Campus/Full-Time

Result: Selected

Difficulty: Medium-Hard

Rounds: 3 rounds

Topics Asked: Data Structures, Algorithms, Machine Learning, Deep Learning, Behavioral, HR, Logical Reasoning, Problem-Solving, Cognitive Abilities

Experience:

This interview experience was for an on-campus recruitment drive for a final-year Computer Science Engineering student. The process consisted of three stages.

Round 1: Gamified Round

This round assessed logical reasoning, problem-solving skills, and cognitive abilities through a gamified format.

Round 2: Coding Round

This round involved two problems:

• Problem 1: A scenario-based question that required a unique application of Dijkstra's algorithm.
• Problem 2: Finding the number of triplets that satisfy a given condition.

Round 3: Technical + HR Round

This was a comprehensive evaluation of technical knowledge, problem-solving abilities, and behavioral attributes.

Technical Interview Questions:

• Project Discussion: As the resume highlighted machine learning (ML) and deep learning (DL) projects, the interviewer explored this domain extensively. Key questions included:
  • Explain your project and the motivation behind it.
  • What ML and DL models did you implement?
  • Write and explain linear regression code for a given dataset.
  • What are Random Forests and Support Vector Machines (SVMs)? Explain their working principles.
  • How did you utilize Numpy and Pandas libraries in your project?
  • How many epochs did you use during training, and why?
  • Did you work with any known datasets? If so, what were their dimensions?
  • Describe the data preprocessing techniques you applied.
• Core Algorithms and Data Structures: The interviewer tested grasp of fundamental algorithms:
  • Implementation and explanation of Dijkstra's algorithm.
  • Discussing real-life applications of Dijkstra's algorithm.

Behavioral and HR Questions:

Assessed communication skills, cultural fit, and motivation through questions such as:

• Why should we hire you?
• What do you know about ANZ?
• Describe a challenge you faced in a project and how you overcame it.
• What are your career aspirations, and how does this role align with your goals?
• General questions on teamwork and problem-solving.

Overall Experience: The interviewing experience was challenging yet rewarding, emphasizing the balance between technical depth and effective communication. The candidate was selected.

Tips: Focus on project clarity, algorithmic problem-solving, and behavioral interview techniques. Master core ML/DL concepts, revise algorithms and data structures, practice behavioral questions using the STAR method, and research the company.