Barclays Data Scientist Interview Questions and Answers

Q1. What do you do if your model has higher false positives
Ans. 
Investigate the model performance metrics and adjust the threshold for classification.
• Analyze the confusion matrix to understand the distribution of false positives.

• Adjust the threshold for classification to reduce false positives.

• Consider using different evaluation metrics like precision, recall, and F1 score.

• Explore feature importance to identify variables contributing to false positives.
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Q1. Explain previous projects
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Q2. Explain 2 algo of your choice
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Q1. Asked about ml algos
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Q1. Types of Chunking in data preparation in RAG
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Q2. How Embedding works in Vector Databases
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Q3. Explain ARIMA model
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Q4. How can we decide to choose Linear Regression for a business problem
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Q1. What is token and it's limit for Open Source LLMs
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Q2. Difference of a Regression and Time Series problem
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Q3. Advantage of LSTM over RNN
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Q4. Performance Metrics for Logistic Regression
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Hard level aptitude questions... Time taking sums

Q1. Puzzles and probability questions
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Q2. Asking completely about data science projects in your resume
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Q1. Just asked some puzzle questions
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Q2. Asked if iam willing to relocate and about my family
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Aptitude test for about an hour.

Q1. Questions related to data science were asked based on your resume. Puzzles were asked too. Mostly they were from gfg.
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Q2. What are parameters used in a random forest.
Ans. 
Parameters used in a random forest include number of trees, maximum depth of trees, minimum samples split, and maximum features.
• Number of trees: The number of decision trees to be used in the random forest.

• Maximum depth of trees: The maximum depth allowed for each decision tree.

• Minimum samples split: The minimum number of samples required to split a node.

• Maximum features: The maximum number of features to consider when
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Q1. Explain Sigmoid Function
Ans. 
Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.
• Sigmoid function is commonly used in machine learning for binary classification problems.

• It is defined as f(x) = 1 / (1 + e^(-x)), where e is the base of the natural logarithm.

• The output of the sigmoid function is always in the range (0, 1).

• It is used to convert a continuous input into a probability value.

• Example: f(0) = 0.5
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Q2. What is a T-test in logistic regression
Ans. 
A T-test in logistic regression is used to determine the significance of individual predictor variables.
• T-test in logistic regression is used to test the significance of individual coefficients of predictor variables.

• It helps in determining whether a particular predictor variable has a significant impact on the outcome variable.

• The null hypothesis in a T-test for logistic regression is that the coefficient of the predi...read more
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Q3. How to fit model to an unexplored market
Ans. 
To fit a model to an unexplored market, conduct thorough market research, gather relevant data, identify key variables, test different models, and continuously iterate and refine the model.
• Conduct thorough market research to understand the dynamics of the unexplored market

• Gather relevant data on customer behavior, market trends, competition, etc.

• Identify key variables that may impact the market and model outcomes

• Test d...read more
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Q1. Central Limit Theorem
Ans. 
Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
• The Central Limit Theorem is essential in statistics as it allows us to make inferences about a population based on a sample.

• It states that regardless of the shape of the population distribution, the sampling distribution of the sample mean will be approximately normally distribut...read more
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