Jio Senior Data Scientist Interview Questions and Answers

Q1. Difference between attention and self attention
Ans. 
Attention focuses on specific parts of input data, while self attention considers relationships within the input data itself.
• Attention is used in models like seq2seq for machine translation to focus on relevant parts of the input sequence.

• Self attention is used in transformer models to capture dependencies between different words in a sentence.

• Attention mechanisms can be either global or local, while self attention is
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Q2. Project related questions
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Q3. Feature engineering
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Q4. Handling null values
Ans. 
Handling null values is crucial for data integrity and analysis.
• Identify null values in the dataset using functions like isnull() or isna()

• Decide on the best strategy to handle null values - imputation, deletion, or flagging

• Impute missing values using mean, median, mode, or predictive modeling techniques

• Delete rows or columns with a high percentage of missing values if they cannot be imputed

• Flag null values to distingu
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Q5. Handling imbalanced training data
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Handling imbalanced training data is crucial for model performance and accuracy.
• Use techniques like oversampling, undersampling, or SMOTE to balance the dataset

• Utilize algorithms that are robust to imbalanced data, such as Random Forest or XGBoost

• Consider using ensemble methods or cost-sensitive learning to address class imbalance
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Q6. Overfitting optimize
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Q7. What is text embeddings
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Text embeddings are numerical representations of text data that capture semantic meaning.
• Text embeddings convert words or sentences into numerical vectors.

• They are used in natural language processing tasks like sentiment analysis, text classification, and machine translation.

• Popular techniques for generating text embeddings include Word2Vec, GloVe, and BERT.
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Q1. Question From Regression, classification and clustering Algorithm.
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Q2. Bagging and boosting
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Q3. How to handel less dataset for regression problems
Ans. 
Use techniques like regularization, feature selection, cross-validation, and data augmentation.
• Utilize regularization techniques like Lasso or Ridge regression to prevent overfitting.

• Perform feature selection to focus on the most important variables and reduce noise.

• Use cross-validation to assess model performance and generalizability.

• Consider data augmentation techniques like synthetic data generation or bootstrapping...read more
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Q4. Python Coding and some scenario based ML question.
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Q1. Question from My project and mentioned skills in resume
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Q2. How will you lead team and what your ways.
Ans. 
I will lead by setting clear goals, providing guidance, fostering collaboration, and recognizing team achievements.
• Set clear goals and expectations for the team

• Provide guidance and support to team members

• Foster collaboration and communication within the team

• Recognize and reward team achievements

• Lead by example and demonstrate strong work ethic
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Q1. Salary and Notice period
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Q1. Deep questions about Machine learning, Deep learning, Generative AI, the working of transformers etc.
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Q1. Deep questions about Machine learning and deep learning with projects done. This was a client round.
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Q1. Salary discussion, project discussion, why change? Why Wipro
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Sql and basic ML , statistics questions

Q1. 4 one on one rounds with case studies and ML questions
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Q1. What is F Score
Ans. 
F Score is a measure of a test's accuracy that considers both the precision and recall of the test.
• F Score is calculated using the formula: 2 * (precision * recall) / (precision + recall)

• It is used in binary classification tasks to balance precision and recall.

• A high F Score indicates a model with both high precision and high recall.
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Q2. What is TFIDF in NLP
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TFIDF stands for Term Frequency-Inverse Document Frequency, a numerical statistic that reflects how important a word is to a document in a collection or corpus.
• TFIDF is used in natural language processing to evaluate the importance of a word in a document relative to a collection of documents.

• It combines two metrics: term frequency (TF) and inverse document frequency (IDF).

• TFIDF helps in identifying the significance of...read more
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Q3. What is cosine similarity
Ans. 
Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space.
• It measures the cosine of the angle between two vectors.

• Values range from -1 (completely opposite) to 1 (identical), with 0 indicating orthogonality.

• Commonly used in text mining for document similarity and recommendation systems.
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Q4. How do you generate embeddings
Ans. 
Embeddings are generated by converting words or entities into numerical vectors in a high-dimensional space.
• Use pre-trained word embeddings like Word2Vec, GloVe, or FastText

• Train your own embeddings using algorithms like Word2Vec, GloVe, or FastText on a large corpus of text data

• Fine-tune pre-trained embeddings on domain-specific data to improve performance
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Q1. Explain BERT Model Architecture and It differs form GPT
Ans. 
BERT is a bidirectional transformer model for pre-training language representations, while GPT is a generative model.
• BERT is a pre-training model that learns contextual representations of words by considering both left and right context.

• GPT is a generative model that uses a transformer decoder to generate text based on the context.

• BERT is bidirectional, meaning it can understand the context of a word by looking at both...read more
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Q1. Almost everything from statistics to EDA & ML models
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Q1. Mostly on projects I've already done, especially on last project and project of the interviewer
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Q1. What are your salary expectations?
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Q2. What is your family background?
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Q3. Share details of your previous job.
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Q4. Why should we hire you?
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Q5. Why are you looking for a change?
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Q6. Tell me about yourself.
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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 conc...read more
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Q1. 1.Discussions on project, one case study given related to data science and other behavioral questions asked.
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