Data Scientist
Interview Date
September 5, 2024
Result
Selected
Difficulty
Not Specified (Challenging)
Rounds
5 stages (2 Online Assessment rounds, Technical Round, Managerial Round, HR Round)
Drive Type
On-Campus/Full-Time (Fresher)
Topics asked
Detailed experience
College: Not Specified
Interview Date: September 5, 2024
Interview Type: On-Campus/Full-Time (Fresher)
Result: Selected
Difficulty: Not Specified (Challenging)
Rounds: 5 stages (2 Online Assessment rounds, Technical Round, Managerial Round, HR Round)
Topics Asked: SQL (self-join, Window Functions, aggregate functions), Database Performance Optimization, Projects (Reinforcement Learning, game logic, policies, reward-penalty fine-tuning), Correlation Matrix, Bellman equations, Scenario-based questions on project, Basic tech questions (MCQs), Non-technical interests, Conflict resolution, Relocation, Work guidelines
The candidate had an on-campus interview with MiQ for a Data Scientist role, which started with two online assessment rounds. The overall experience was good and challenged the candidate's abilities to understand the math behind ML concepts.
First Round: Technical
The round began with the "Tell me about yourself" question, where the candidate technically introduced themselves and discussed their 8th-semester major project, leading to 3-4 questions on it. The interview then moved to an easy SQL question solvable with a self-join, followed by a medium-level question involving Window Functions and aggregate functions like AVG and COUNT. After the SQL queries, a question about database performance optimization was asked. Tricky questions were asked in these topics, requiring a good grasp of basic concepts.
Managerial Round
This round was conducted by the hiring team manager and focused heavily on the candidate's resume. Basic tech questions, presented like MCQs, were asked. A significant portion of the discussion revolved around the candidate's minor project, which was a reinforcement learning project. Topics covered included game logic, policies, reward-penalty fine-tuning, correlation matrix, and Bellman equations. The manager concluded by presenting a scenario from the project and asking how it would affect the policy and model's learning.
HR Round: Final
This round started with basic questions about primary and secondary schooling, parents' work and occupation, and non-technical interests. Situation-based questions involving conflict resolution were also posed. Finally, questions on relocation and work guidelines were asked.
Results: The candidate was selected for the role, being the sole final selection from 300 initial applicants, which they described as a surreal feeling.