MasterCard Senior Data Scientist Interview Questions and Answers

Given Python coding challenge related to string processing and second question was matrix processing using python

Q1. Bagging boosting and its difference and uses.
Ans. 
Bagging and boosting are ensemble learning techniques used to improve the performance of machine learning models by combining multiple weak learners.
• Bagging (Bootstrap Aggregating) involves training multiple models independently on different subsets of the training data and combining their predictions through averaging or voting.

• Boosting involves training multiple models sequentially, where each subsequent model correc...read more
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Q2. Methods to minimize overfitting and underfitting
Ans. 
To minimize overfitting, use techniques like cross-validation, regularization, early stopping. To minimize underfitting, increase model complexity, gather more data.
• Use cross-validation to evaluate model performance on different subsets of data

• Apply regularization techniques like L1 or L2 regularization to penalize large coefficients

• Implement early stopping to prevent the model from training for too long

• Increase model ...read more
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Q3. Transformer architecture
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Q4. LLM usecase and explain how to work on it
Ans. 
LLM usecase involves using Latent Linear Models for various data analysis tasks.
• LLM can be used for dimensionality reduction in high-dimensional data.

• LLM can be used for clustering similar data points together.

• LLM can be used for anomaly detection in datasets.

• LLM can be applied in natural language processing tasks such as text classification.

• LLM can be used in recommendation systems to predict user preferences.
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Q1. Given data, find out what AI project can be discovered and how to progress on implementing it.
Ans. 
Identify AI projects from data analysis and outline steps for implementation.
• Analyze the data to identify patterns or trends that suggest potential AI applications.

• Consider projects like predictive analytics for customer behavior or anomaly detection in fraud detection.

• Engage stakeholders to understand business needs and align AI projects with strategic goals.

• Prototype a small-scale version of the AI solution to valida...read more
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Q2. Given data and usecase, which model to use to implement solution and how detailed steps.
Ans. 
Choosing the right model involves understanding the data, use case, and desired outcomes.
• 1. Understand the problem: Is it classification, regression, clustering, etc.? Example: Predicting patient outcomes (classification).

• 2. Analyze the data: Check for missing values, data types, and distribution. Example: Use EDA to visualize patient demographics.

• 3. Select a model: Based on the problem type, choose a model. Example: U...read more
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Q1. Discussed each project in my resume in detail and related questions
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Q2. Discussed team and personal behavioural questions
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Q1. Basic Question on Machine Learning Algorithms.
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Q2. End to End project that is mostly focused on Scaling up and deployment purposes
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Q1. Company fit and expectations of Compensation etc.
Ans. 
Company fit is crucial for long-term success. Compensation expectations should align with industry standards and experience.
• Research the company culture and values to ensure alignment with personal values and work style.

• Understand the company's expectations for the role and how your skills and experience can meet or exceed them.

• Discuss compensation openly and transparently, considering industry standards, your experien...read more
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