Dunnhumby Lead Applied Data Scientist Interview Questions and Answers

Sql and basis python

Business related case study

Q1. Previous Project discussion
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20 MCQ questions related to Data science and 1 coding ML question.

Q1. What is overfitting and underfitting?
Ans. 
Overfitting and underfitting are common problems in machine learning where a model performs too well on training data but poorly on unseen data, or performs poorly on both training and unseen data due to oversimplification.
• Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor generalization on unseen data.

• Underfitting happens when a model is too simple...read more
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Q2. What is K-fold CV?
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K-fold CV is a technique used to evaluate the performance of a machine learning model by splitting the data into k subsets.
• Data is divided into k subsets, with one subset used as the validation set and the rest as training sets.

• The process is repeated k times, with each subset used once as the validation data.

• The average of the k validation results is used as the final performance metric.

• Helps in reducing bias and vari...read more
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It was brain teasing and more of stats

Basic coding questions related to data science

Q1. How to handle imbalanced data in text analytics?
Ans. 
Imbalanced data in text analytics can be handled by techniques like oversampling, undersampling, and SMOTE.
• Use oversampling to increase the number of instances in the minority class

• Use undersampling to decrease the number of instances in the majority class

• Use SMOTE to generate synthetic samples for the minority class

• Use cost-sensitive learning algorithms to assign higher misclassification costs to the minority class

• Use...read more
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Q1. Business case study related to Data science
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Python Coding Questions with

Bubble sort example Question using python

Previous projects based questions

The coding round was relatively easy. A sufficient amount of preparation on LeetCode should be beneficial, along with reviewing the sample questions.

Q1. All theoretical questions about Linear Regression
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Q2. Past projects
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2 sections. First, multiple choice questions regarding Ml algo, SQl. Second, ML coding problem.

Q1. Details about dimensionality reductions, the difference between bagging and boosting techniques, details about XgBoost, and metrics to choose for the given Ml classification problem.
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Q1. Interview with HR manager, behavioural questions.
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Q1. Interview with business leadership.
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Q1. Initially asked to introduce myself and my work experience.
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Q2. Based on my work experience, the panel asked further questions around my projects, going deeper into the basics of machine learning algorithms that I used for regression models and time series forecasting ...read more
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Q1. The second round was more of like techno-managerial round where the panel asked me how I did my latest project, what were the obstacles faced, how did I overcome them, etc.
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Q1. The final round was with the HR where the salary negotiation part happened.
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