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Sr Data Scientist Interview Questions
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What useful library did you use in python ?(other than Pandas and Numpy)
Basic ML Regularization Over fitting, Under fitting, XGBOOST, RF differences. boosting and bagging etc.
it was a take home project about defining metrics
HackerRank coding - 4 super lengthy question to be done in 90 mins. 1 question of sql and rest 3 on python. Technical interview: mostly project related discussions and few theoretical questions Director interview: All non-tech questions based on behavior, culture etc.
We have sales at various price points for multiple SKU's. There is lots of missing data because all SKU's are not available at all prices. There is also a time nature to the data. How do you model for the best price point to optimize profits?
Q: Tell us about yourself and your experience Q: Walk us through your past projects and experience. And later asked technical and non-technical questions based on the resume. Q: ML algos were asked Q: SQL questions to solve Q: Scenario bases DS questions. Which model would I choose, why and walk them through Q: Product-based scenarios were given and my inputs were asked on how I could make it better or how would I have navigated from scratch. Q: Ways to handle an unbalanced dataset
"Given several methods to achieve an end goal, identify the pros and cons, costs, time spent, and the best way to measure outcomes. How do we decide which method performs better?"
round 1 - why do you want to change the job? round 2 - SQL queries (CTEs + window functions medium level) + basic data science questions + project round 3 - credit risk modelling take home assignment + technical discussion round 4 - credit risk case study discussion round 5 - absolutely useless round (and they rejected me for no reason here after doing well throughout the process) Whole process took 2-3 months which is unnecessarily long
What is an activation function?
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