Senior Data Scientist

Senior Data Scientist Interview Questions

Updated 18 May 2024

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Interview Questions

  • Q1. Print rows where a certain criterion is met (ex - in a dataset of employees select the ones whose salary is greater than 100000 - in SQL)

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  • Q2. Extract only India Players from dictionary (using list comprehension) CSK = {"Dhoni" : "India", "Du Plessis" : "South Africa", "RituRaj": "India", "Peterson" : "England", "Lara" : "West Indies"}

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  • Q3. How do you print the 3, 5 and 7th row in a database (Python - use Pandas)

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  • Q4. What are the types of regression models, name them and explain them

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  • Q5. What is ETL and what are the types or examples of ETL tools

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  • Q6. Find Common Elements in three lists using sets arr1 = [1,5,10,20,40,80,100] arr2 = [6,7,20,80,100] arr3 = [3,4,15,20,30,70,80,120]

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  • Q7. Print rows with the same set of values in column (these are not duplicates row - just the duplicates values in a column)

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  • Q8. Split Dataset in train, test and validation (import library and split dataset)

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  • Q9. Print Unique values in the dataset and delete the duplicate rows (SQL)

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  • Q10. Print rows with the second highest criterion value without using offset function in SQL

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+4 interview questions

Interview Questions

  • Q1. 1. 2 python coding questions. First one was to find common elements between given 2 arrays. Second was to reverse the string and return back. 2. Discussions on project, Machine learning, Deep learning concepts, Stats.

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  • Q2. Explain BERT Model Architecture and It differs form GPT

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  • Q3. 1.Discussions on project, one case study given related to data science and other behavioral questions asked.

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  • Q4. What is lamda function in python?

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  • Q5. What is p value? What is spacy, NLP, NER model?

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Interview Questions

+1 interview question

Interview Questions

  • Q1. The second round was more of like techno-managerial round where the panel asked me how I did my latest project, what were the obstacles faced, how did I overcome them, etc.

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  • Q2. Details about dimensionality reductions, the difference between bagging and boosting techniques, details about XgBoost, and metrics to choose for the given Ml classification problem.

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Interview Questions

Interview Questions

  • Q1. How do you handle large amount of data in financial domain?

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  • Q2. Tell me about anomaly detection problem? What is LSTM? Why do you need BERT in the chatbot?

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  • Q3. Tell me about preprocessing techniques? How can you resolve over fitting problem?

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  • Q4. How would you use handle outlier and unbalanced dataset?

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Interview Questions

  • Q1. Basically here they try to check your communication and basic understanding of technical knowledge and how would you explain to client

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  • Q2. Machine learning basic question which accuracy metric n all

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  • Q3. Sql query questions like group by n all

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  • Q4. Introduction of the candidate

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Interview Questions

Interview Questions

Interview Questions