Myntra Data Scientist Interview Questions and Answers

Q1. Tell me about your projects?
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Q2. How do you approach the project if you are using logistic regression model?
Ans. 
Approach involves data preprocessing, model training, evaluation, and interpretation.
• Perform data preprocessing such as handling missing values, encoding categorical variables, and scaling features.

• Split the data into training and testing sets.

• Train the logistic regression model on the training data.

• Evaluate the model using metrics like accuracy, precision, recall, and F1 score.

• Interpret the model coefficients to under...read more
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Q1. What are you future goals?
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Q2. What would you do if your interested field doesnt have any work in the company?
Ans. 
I would seek opportunities to apply my skills in related fields within the company.
• Explore other departments or teams within the company that may have projects related to my field of interest

• Offer to collaborate with colleagues in different departments to bring a new perspective to their projects

• Seek out professional development opportunities to expand my skills and knowledge in related areas
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Build a simple cat and dog image classifier

Q1. ML related questions
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Q1. Behavioural questions, basically to check if you will be fit in a team
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Q1. Basic Recommendation System Questions
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Q2. A B Testing Questions
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Questions related to basic coding were asked, and some background on projects and discussions alongside maths and statistics concepts

Q1. Questions related to projects done at my previous company
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Q1. Questions related to my work at previous company
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Q2. ML system design use case type of discussion
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Easy topics arrays, sequence sum.

SQL and Python data analysis questions

Q1. How do you match the data in different columns.
Ans. 
Matching data in different columns involves comparing the values in the columns and identifying similarities or patterns.
• Use string matching techniques like exact match, partial match, or fuzzy matching.

• Apply data cleaning and preprocessing techniques to standardize the data before matching.

• Utilize advanced algorithms like Levenshtein distance or Jaccard similarity for more complex matching.

• Consider using database join...read more
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