Interviews are becoming more complex to evaluate candidates. Every candidate has their own strengths/weakness. We need to identify the strength of the candidate throughout the discussion. My approach is to discuss the projects/domains and understand perspectives.
- How much they have solved a use case end to end
- What learning resources/techniques/papers were leveraged
- Data Science models were built
- What are the dataset challenges/data collection/accuracy issues?
- Was it deployed / what were learning's
- How the end goal/metrics were evaluated
I still find it hard to remembers the best case / worst case for sort/search / B+ tree after 20 years. I don't really evaluate candidates based on MCQ questions. There are too many facts which google query can return in milliseconds.
In the process of interviews, we are looking at candidates on solving use cases, looking at design perspectives from implementation perspectives, think ahead from all aspects, technical and domain-based learning. Good communication alone cannot identify what you say vs what you actually know. We need to walk through, discuss, and understand each other perspectives to arrive at the clarity of thoughts, communication, ideas, and learning.
Being empathetic, open-minded, patient listener, few basic questions on each concept to know its theoretical vs insightful vs implementation knowledge can provide clarity on the candidate's perspectives of learning/awareness.
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Keep Thinking!!!
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