Slice Data Scientist Interview Questions and Answers

Q1. DBSCAN Clustering
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Q2. How does current work helps solving use cases for PayU
Ans. 
My current work involves analyzing transaction data to identify patterns and trends, which can help PayU optimize their payment processing services.
• Analyzing transaction data to identify fraudulent activities and improve security measures for PayU

• Developing predictive models to forecast transaction volumes and optimize payment processing times

• Utilizing machine learning algorithms to personalize user experiences and inc...read more
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Q1. Why are you interested in this role
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Q2. Provide an example how you solved a problem
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I solved a problem by using machine learning algorithms to predict customer churn for a telecom company.
• Identified relevant data sources such as customer demographics, usage patterns, and customer service interactions.

• Preprocessed and cleaned the data to handle missing values and outliers.

• Built and trained a machine learning model using algorithms like logistic regression and random forest.

• Evaluated the model's perform...read more
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Basic questions like stats, probability etc

Scenario based question

Q1. Questions on fundamentals
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Q1. General questions
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3 DSA Que was asked on hackerrank

Q1. 3 DSA que asked by interviewer wirh code
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Q1. CV walthrough and project related questions. Aptitude questions.
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Q1. Modelling related questions like what metrics to track for model performance, AI ML logics, sql joins etc.
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Q1. Culture fitment
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Cancellation in the number of bookings in an app has increased exponentially in the past few weeks, How would you conduct an RCA on the same.

Q1. What is rnn and lstm
Ans. 
RNN stands for Recurrent Neural Network and LSTM stands for Long Short-Term Memory. They are types of neural networks used for sequential data processing.
• RNN is a type of neural network that can process sequential data by maintaining a memory of past inputs.

• LSTM is a type of RNN that can handle the vanishing gradient problem and can remember long-term dependencies.

• LSTM has gates that control the flow of information int...read more
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Q2. What is precision and recall
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Precision and recall are two important metrics used to evaluate the performance of a classification model.
• Precision measures the proportion of true positives among all the predicted positives.

• Recall measures the proportion of true positives among all the actual positives.

• Precision and recall are inversely related and a trade-off exists between them.

• A high precision means that the model is good at predicting positive ca...read more
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Q3. What is regularisation
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Regularisation is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function.
• Regularisation helps to control the complexity of a model and reduce the impact of irrelevant features.

• It adds a penalty term to the loss function, which encourages the model to have smaller weights.

• There are different types of regularisation techniques such as L1 (Lasso) and L2 (Ridge) regularisa...read more
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