1. Tell me about the hardest project you worked on. What did you learn? 2. What are some data quality issues? How do we resolve them? 3. Resume walkthrough. 4. What would you do if your model quality drops after 4 months of deploying?
Data Science Internship Interview Questions
10,899 data science internship interview questions shared by candidates
1. Your train/test event rate is 10% but oot test data event rate is 5%. Why did you build the model when with 1:10 event rate without checking the real time data for the next 6 months, which is 5%? - I have 14 years of exp including 8 years of consulting. This is the worst interview question I ever had. 2. This question was asked 3 times. You are building a prospect model to target clients for house refinances, at what stage are you building the model? Underwriting, funding? - It was a surprise to receive such questions in interview. I told them that we are identifying the prospects for marketing before even they reach out/start refinancing but they kept asking me the same questions. Looks like they just want to prove they know couple of things about mortgage industry but it was stupid. 3. I mentioned about a summarization project build on llama2 for my finance company built using call transcripts. Interviewer accused me of lying as he thinks I can't build model because ppl discuss ssn numbers and the details about other companies. Seriously? You are in a senior position and you don't know what's synthetic data or how customer data is redacted internally etc? 3. They are super rude from the start during the project review. I lost interest after 15 minutes as they are constants demeaning my current company and the projects we do when they don't even understand the basics of DS.
SQL, machine learning, evaluation metrics
What is linear regression in data science?
What do you know about the company? What sort of data analysis have you done? What tools do you use to perform your data analysis?
How would you select features to input into a ML model?
How do you handle missing values
Basic Data Science principles and statistics
How would you deal with data missingness?
How do you manage your time/prioritize tasks ?
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