Track: Data Science / ML

Data Science / ML - Interview Briefing

Overview

Tiger Analytics evaluates Data Science and ML candidates through a structured process that includes coding rounds, technical rounds, an online assessment (OA), and an HR round. The focus is on problem-solving, algorithmic thinking, and domain-specific knowledge, with candidates often facing rejection due to incomplete solutions or time constraints.

Round Breakdown and Long-Tail Questions

1. Coding Round

• Duration: 45 minutes
• Format: 2 coding questions (1 Easy-Medium, 1 Medium)
• Outcome: Candidates are expected to solve at least one question fully and partially solve the second.
• Source: LeetCode Discussion

2. Technical Round 1

• Duration: 45 minutes
• Format: 2 coding questions and 1 complex SQL query
  • Coding Questions:
    1. Factorial of a number
    2. Matrix multiplication
  • SQL Query: Complex query requiring advanced joins, aggregations, or window functions.
  • Additional Questions: Basic SQL and Python concepts (e.g., loops, functions, data structures).
• Outcome: Candidates must demonstrate proficiency in algorithmic problem-solving and SQL.

3. Technical Round 2

• Duration: 45 minutes
• Format: 2 coding questions and project discussion
  • Coding Questions:
    1. Best time to buy and sell stock (LeetCode problem)
    2. Shorten a string based on frequency (custom problem)
  • Project Discussion: Candidates are expected to explain their projects, methodologies, and problem-solving approaches.
• Outcome: Strong emphasis on optimization and clarity of thought.

4. Online Assessment (OA)

• Duration: 1 hour
• Format: 2 coding questions and ML-based MCQs
  • Coding Questions:
    1. Medium-level problem (e.g., dynamic programming, graph traversal)
    2. Partially solved problem (e.g., incomplete solution or time constraints)
  • MCQs: Questions covering ML concepts (e.g., supervised learning, model evaluation, feature engineering).
• Outcome: Candidates must balance coding speed and conceptual understanding.

5. HR Round

• Duration: 30 minutes
• Format: Discussion about expectations, salary, and role alignment
• Outcome: Candidates may reject offers due to existing offers or salary expectations.

Key Takeaways

• Focus Areas: Algorithmic problem-solving, SQL, Python, and ML concepts.
• Common Pitfalls: Incomplete solutions, time management, and lack of optimization in coding problems.
• Success Criteria: Demonstrated proficiency in at least 70% of the questions, clarity in communication, and alignment with role expectations.