Track: Data Science / ML

Data Science / ML - Ey Interview Briefing

Overview

The Data Science / ML role at Ey prioritizes hands-on technical skills in Python, SQL, Pandas, and Excel, with a strong emphasis on practical problem-solving and data manipulation. The interview process includes a technical round, behavioral assessment, and HR discussion, where candidates must demonstrate both technical proficiency and alignment with role expectations. Mismatches in domain expertise or salary expectations often result in rejections.

Round Breakdown & Questions

1. Technical Round (70 minutes)

Focus: Python, SQL, Pandas, and Excel Format: Coding questions, SQL queries, Pandas operations, and Excel tasks.

Key Questions Asked:

• Python:
  • Solve 2 Python coding questions involving array separation using lambda functions and regular expressions.
• SQL:
  • Write SQL queries involving joins and retrieving names containing the letter 'R'.
• Pandas:
  • Create datasets, merge dataframes, and drop missing values.
• Excel:
  • Explain the difference between Vlookup and Hlookup.
  • Use Vlookup to retrieve specific values.
  • Create a pivot table and perform count operations.

Tools Used: Jupyter Notebook for coding tasks.

2. Behavioral Round (30 minutes)

Focus: Personal and opinion-based questions Format: Open-ended discussion on workplace conflicts and societal opinions.

Key Questions Asked:

• What is your opinion on live-in relationships?
• Describe any conflicts you have faced in the workplace and how you resolved them.
• What is your opinion on current Maharashtra politics?

3. HR Round (15 minutes)

Focus: Role alignment and salary expectations Format: Discussion on role responsibilities and compensation.

Key Points:

• Candidate was informed about selection but faced a salary negotiation barrier.
• The role involved Power BI and Excel, which was misaligned with the candidate's current domain.

Key Takeaways for Candidates

• Technical Preparation: Focus on Python, SQL, Pandas, and Excel. Practice coding questions on platforms like LeetCode and ensure familiarity with data manipulation tasks.
• Behavioral Preparation: Be prepared for unconventional questions that assess your problem-solving approach and workplace adaptability.
• Role Alignment: Clearly understand the role’s expectations, especially regarding domain expertise and tools used (e.g., Power BI vs. ML-specific tools).
• Negotiation Strategy: Research salary benchmarks and be prepared to negotiate effectively to avoid last-minute rejections due to compensation mismatches.