1- Machine Learning concepts 2- Data Structure Case Study (Very easy) 3- Coding (Easy but they need hands-on exp) And Finally, Leadership (14 principles)
Data Scientist Interviews
Data Scientist Interview Questions
In a data scientist interview, expect employers to ask questions that assess your data modeling, problem-solving, and programming skills. Be prepared to answer general questions that test your knowledge of statistics and data science. You should also be ready to answer open-ended questions that test your creativity, communication skills, and formal education in data modeling and programming.
Top Data Scientist Interview Questions & How to Answer
Question 1
Question #1: Which data modeling techniques do you prefer and why?
How to answer
How to answer: Turning data into understandable and actionable information is a critical part of the data scientist's job. This question allows employers to understand your data modeling skills and background. List and discuss your preferred data modeling techniques, including benefits such as ease of use, flexibility, etc.
Question 2
Question #2: How would you detect bogus Instagram accounts used for scamming consumers?
How to answer
How to answer: Questions like this one allow an employer to test your problem-solving skills. When answering open-ended questions such as these, feel free to ask clarifying questions and use whiteboards to demonstrate your coding and diagramming skills. Share your thought process as you work through the problem.
Question 3
Question #3: Describe circumstances that require a list, tuple, or set in Python.
How to answer
How to answer: Interviewers will use questions such as this one to test your Python programming skills. Review Python basics such as lists, tuples, and sets before your interview. You should be able to explain when and how each tool is used by data scientists.
54,212 data scientist interview questions shared by candidates
How to build a summary table out of a written-in-a-notepad-document table of cricket wins and losses by country. How to check the validity of an IP address string given some list of constraints.
How to reduce number of variables in Logistic regression and random forest?
The interviewer asked about random forest and how it works. When I said that each decision tree in the forest considers a random subset of features, he disrespectfully interrupted me and told me that I am wrong. Then he scolded me for giving the "wrong" answer.
If you're trying to predict the gender of your customers and you only have 100 data points, what are possible problems?
Q: Given a function with inputs --an array with N randomly sorted numbers, and an int K, return output in an array with the K largest numbers. Q: 1. How does GMM/HMM work 2. Name some dimensional reduction method; I said PCA and we talked a bit about how PCA works and what's the physical intuiation 3. How K-means work, what kind of distance metric would you choose, what if different features have different dynamic range 4. How GMM works (EM algorithm)
En quel animal vous réincarneriez-vous ?
Model Deployment. Credit Risk(A lot of credit risk concept) Risk Modeling.
You have a database of customer transactions and for some users you don’t have much data (1-2 transactions), and you want them to use Revolut’s services more. How would you analyse the data to do so, with such limited data on some users.
- A Bays textbook example (fair and unfair coins) - Some python simple algorithm codes and calculating the complexity of the method - Some ML questions about different methods and how the learning happens and objective functions ....
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