Round 1: screening interview with the internal recruiter. They had a few technical questions given to them by the hiring team. Simple things like how do you create an ETL pipeline? Basics of AB testing Round 2: Very intense 2 hour interview. Part 1 focussed on how my experience aligns with Barclays values and ideas such as risk, change and transformation, and discussion of my data science related work. I was asked to answer questions in the STAR format. Part 2 was technical round with four questions. First question - gave me some data and asked to do EDA. Second question - asked me to review the code for an ML model they had. Third - asked me about how to implement an A/B test for a customer churn problem. Round 3: Interview with a senior Director level person. More broad questions around strategy and risk. E.g.: How is the field of ML/AI evolving and what implications it might have for the bank in the next 2-3 years?
Sr Data Scientist Interview Questions
3,383 sr data scientist interview questions shared by candidates
explain past projects
What ML projects I have worked with in the recent past
Give me a data science project and describe it end to end. What was the situation, what models and model assumptions, how did you tune the model? (Others that i cant remember)
What's the purpose of using pooling and padding in ConvNets?
Basic OOP, how to find a min in an array, posible exception in some code, etc.
What’s an interesting metric from a project of your choice? Why?
general project experience and data science related knowledge.
How do you design an A/B test.
Given a pandas data-frame "df" with five float columns the matplotlib command pyplot.plot(df) will...
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