What is your take on competitor strategy in terms of Machine Learning?
Lead Data Scientist Interview Questions
347 lead data scientist interview questions shared by candidates
For this position, most of the questions were directed towards management skill and philosophy. Only few technical questions, possibly because of my academic background and my similar roles.
Asked about experience in Time Series Algorithms
They sent a link to some publicly-available data and told me to tell them something interesting about it.
1: Trees-based methods are sometimes called greedy algorithms. Can you explain what greedy means, and then explain what is a potential downside of using a greedy algorithm? 2) Can you explain the concept of multicollinearity and how it can impact a model? Then discuss how you measure the amount of multicollinearity. Finally, using the measure you identified, how much multicollinearity would cause concern for a model, and why? 3) A colleague approaches you with an idea to run a marketing mailer experiment. The colleague wants to test the impact of two different marketing mailers (let’s call them Mailer A and Mailer B) on life insurance sign-ups. The colleague asks you: “how many mailers do I need to send out to have a statistically significant sample?” How do you respond? What questions would you ask the colleague to help answer their question? 4) Assume you deployed a binary classification model and after 12 months your business partner asks you to verify the model is still performing as expected. What steps would you take to validate the deployed model’s performance? What different recommendations would you give your business partner if the model is performing as expected versus if the model is showing deteriorated performance? 5) Please share what you like about the open role. 6) Can you tell us about your prior mentoring or leadership experiences? 7) why statefarm .
What is your ideal job?
Case studies related to MPL problems like recommendation systems, fraud detection etc
Based on your Resume. Deep Learning, Machine Learning Transformer etc
Impute missing values and what is the impact of other imputations on model performance.
How is Feature Engineering performed?
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