Case study on some data science topic. Overfitting vs. underfitting. Model deployment. Some GenAI stuff etc.
Sr Data Scientist Interview Questions
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Train a binary classifier along with validation, etc.
1. 3 words to describe myself 2. how to achieve regularization in neural networks 3. how to evaluate multi label problems e.g. search & recommendation
Technical interview had questions on deep learning, model evaluation, architecture
I was asked to write a string character counting code.
Pandas coding for a dataset
1. Introduce yourself 2. Discuss a project you worked on recently. 3. What do you know about us nad how can you add value to what we do
Talk about my most relevant technical project
Online coding: Imo the most difficult part of the process due to some awkward wording. Also a very quick fire exercise, essentially run out of time. 3 parts: SQL, Python, generic ML knowledge. Past tech experience: just make sure that you know what you did and why (may require brushing up on DS knowledge). Tech case interview: 'This is a problem, how would you build a DS model around it', from there a rather normal case. Personal Experience: same as for generalist consultant Final case + personal experience: same as for generalist.
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