The question was a jupyter notebook that had a preloaded dataset into it. The dataset was not cleaned and need lots of pre-processing done on it. You were tasked with building a model and explain it.
Science Interview Questions
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"How would you explain the advantages of WatsonX to a non-technical client?" "Can you walk us through how you would handle a client objection during a technical sales pitch?" "What are the key differences between WatsonX and competing AI platforms like AWS SageMaker or Azure AI?"
Q: What is your proficiency in SQL and Python? Q: What is the current macroeconomic trend and how does that affect AMEX?
1st stage is a non-technical interview with a manger 2nd stage assignment 3rd stage techinical interview with data scientist 4th psychometric test (4 kinds including math, reasoning, personality and verbal test)
Explain how to handle high cardinal categorical variables
Rationalize a fraction involving radicals. Identify the function of a specified enzyme. Identify the proportion of a given nucleotide in a genome given the percentage of another.
Best model to use for this dataset? Steps to work with a dataset?
How to figure out whether a transaction is fraudulent? How to predict whether a customer will call us in near future for any query?
Have you worked with time series models before? If so, please elaborate.
tell us about why you are best fit for this role
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