Tesco Senior Data Scientist Interview Questions and Answers

Q1. Tell me aboutself
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Q2. Previous experience
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Q1. Basics questions about your self and academic
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Q2. Hiring T@C Salary negotiation
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Q3. About location of job posting
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Q4. Job profile disscussion
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Q5. Working time and culture
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Q6. Working time after joining
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Q1. Why we should hired you ?
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Q2. Your strength and weakness
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Q3. Your last working experience
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Q1. Ap overview and journals
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Q2. Inquiry GRIR and Ageing
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Array-based question

Q1. Explain Xgboots
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Q2. Explain random forest
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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. Intermediate level SQL questions using joins and case when
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Intermediate SQL question focusing on joins and case when statements for data manipulation.
• Joins combine rows from two or more tables based on a related column. Example: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id.

• CASE WHEN statements allow conditional logic in SQL queries. Example: SELECT name, CASE WHEN age < 18 THEN 'Minor' ELSE 'Adult' END AS age_group FROM users.

• INNER JOIN returns only matching ro...read more
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Q2. String manipulation advanced level question in python
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Q1. Past experience
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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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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...read more
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Q1. Why do you want to join?
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