Alshaya Group Data Scientist Interview Questions and Answers

Q1. Past experience
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Array-based question

Q1. Explain Xgboots
Ans. 
XGBoost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting.

• It is an implementation of gradient boosted decision trees designed for speed and performance.

• XGBoost is widely used in machine learning competitions and real-world applications.

• It can handle missing data, regularization, and parallel processing efficiently.

• XGBoost ...read more
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Q2. Explain random forest
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Random forest is an ensemble learning method that builds multiple decision trees and combines their predictions.
• Random forest is a type of ensemble learning method.

• It builds multiple decision trees during training.

• Each tree in the forest makes a prediction, and the final prediction is the average or majority vote of all trees.

• Random forest is used for classification and regression tasks.

• It helps reduce overfitting and ...read more
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Q1. Explain about your project
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Q2. Explain the sequence to sequence models and transformers
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Sequence to sequence models are used in natural language processing to convert input sequences into output sequences.
• Sequence to sequence models are commonly used in machine translation tasks, where the input is a sentence in one language and the output is the translated sentence in another language.

• Transformers are a type of sequence to sequence model that use self-attention mechanisms to weigh the importance of diffe...read more
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Q1. Bias and variance with respect to model
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Bias and variance are two types of errors that can occur in a model.
• Bias refers to the error introduced by approximating a real-world problem, leading to underfitting.

• Variance refers to the error introduced by modeling the noise in the training data, leading to overfitting.

• Balancing bias and variance is crucial for creating a model that generalizes well to unseen data.
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Q2. Hypotheses test
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Q1. Heavy SQL solutions
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Retail case study, with soft skills is required for this round

Q1. Round one was a combination of a technical round and a discussion on previous work experience. Questions - Previous work experience, Basic SQL questions, Basic Python questions, Basic Tableau question...read more
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Q1. This was a culture fit round. This was a one on one discussion with the manager. Offer letter was rolled out after this round.
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Python and sql based questions

Case Study was related to customer propensity to buy.

Q1. Linear Regression Assumptions
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Linear regression assumptions include linearity, independence, homoscedasticity, and normality.
• Assumption of linearity: The relationship between the independent and dependent variables is linear.

• Assumption of independence: The residuals are independent of each other.

• Assumption of homoscedasticity: The variance of the residuals is constant across all levels of the independent variables.

• Assumption of normality: The resid...read more
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Q2. ML Algorithms and Evaluation Metrics.
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Q1. What is VIF(variance inflation factor)
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VIF is a measure of multicollinearity in regression analysis, indicating how much the variance of an estimated regression coefficient is increased due to collinearity.
• VIF values greater than 10 indicate high multicollinearity

• VIF is calculated for each predictor variable in a regression model

• VIF is calculated as 1 / (1 - R^2) where R^2 is the coefficient of determination from regressing a predictor on all other predicto
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Q1. Why do you want to join?
Ans. 
I am impressed by your company's innovative projects and collaborative work culture.
• I admire the company's commitment to cutting-edge technology and data-driven solutions.

• I am excited about the opportunity to work with a talented team of data scientists and researchers.

• Your company's reputation for fostering a collaborative and inclusive work environment is appealing to me.
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Q1. Intermediate level SQL questions using joins and case when
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Q2. String manipulation advanced level question in python
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