Diageo Data Scientist Interview Questions, Process, and Tips

Included aptitude, coding, english grammar, technical questions

Easy sql and python questions

Q1. Librarires Used
Ans. 
I have experience using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib for data analysis and visualization.
• Pandas for data manipulation

• NumPy for numerical operations

• Scikit-learn for machine learning algorithms

• Matplotlib for data visualization
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Q2. Previous work experiemce
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Coding test link will sent and involves python and data science questions

Q1. Technical lead will ask questions in ML or data science depending the role you applied
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Their will case study question which you need to answer from technical manager.

Q1. Just normal HR discussion on salary
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Q1. Bio. SQL Basics. Python Basics. Projects done.
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Q1. Similar to Round 1 along coding basics and ML
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Decide which video clip will work at a campaign

Q1. What was your projrct about ?
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Q2. What approach did you use and why ?
Ans. 
I used a combination of supervised and unsupervised learning approaches to analyze the data.
• I used supervised learning to train models for classification and regression tasks.

• I used unsupervised learning to identify patterns and relationships in the data.

• I also used feature engineering to extract relevant features from the data.

• I chose this approach because it allowed me to gain insights from the data and make predicti
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Q3. Why was this model/ approach used instead of others ?
Ans. 
The model/approach was chosen based on its accuracy, interpretability, and scalability.
• The chosen model/approach had the highest accuracy compared to others.

• The chosen model/approach was more interpretable and easier to explain to stakeholders.

• The chosen model/approach was more scalable and could handle larger datasets.

• Other models/approaches were considered but did not meet the requirements or had limitations.

• The chos...read more
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Q4. How did you prevent your model from overfitting ? What did you do when it was underfit ?
Ans. 
To prevent overfitting, I used techniques like regularization, cross-validation, and early stopping. For underfitting, I tried increasing model complexity and adding more features.
• Used regularization techniques like L1 and L2 regularization to penalize large weights

• Used cross-validation to evaluate model performance on different subsets of data

• Used early stopping to prevent the model from continuing to train when perfo...read more
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If you were to design a tool that splits the budget across brands and vehicles, how would you go about this.
Apart from this, i was asked about why i was joining this company.

Q1. What are your career aspirations ?
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Q2. Couple of more case studies
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Q1. 1. Questions from time series forecasting. 2. How to forecast demand if we have no data like for the period of Covid pandemic. 3. Questions from NLP.
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Q1. Scenario based question with technical challenges.
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Q1. Questions from statistics
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Q1. It was mostly salary negotiation.
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