Sigmoid Senior Data Scientist Lead Interview Questions and Answers

Q1. How does dropout help in neural networks?
Ans. 
Dropout helps prevent overfitting in neural networks by randomly setting a fraction of input units to zero during training.
• Dropout helps in preventing overfitting by reducing the interdependence between neurons

• It acts as a regularization technique by randomly setting a fraction of input units to zero during training

• Dropout forces the network to learn redundant representations, making it more robust and generalizable

• It ...read more
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Q2. How does xgboost deal with nan values?
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XGBoost can handle missing values (NaN) by assigning them to a default direction during tree construction.
• XGBoost treats NaN values as missing values and learns the best direction to go at each node to handle them

• During tree construction, XGBoost assigns NaN values to the default direction based on the training data statistics

• XGBoost can handle missing values in both input features and target variables
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Q3. What would you do if you see your model not performing well on a time series prediction specifically on peaks and troughs?
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Q4. How would you deal with datasets having lots of categories
Ans. 
Utilize feature engineering techniques like one-hot encoding or target encoding to handle datasets with many categories.
• Use feature engineering techniques like one-hot encoding to convert categorical variables into numerical values

• Consider using target encoding to encode categorical variables based on the target variable

• Apply dimensionality reduction techniques like PCA or LDA to reduce the number of features

• Use tree-b...read more
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Case study involved creating a churn model with an imbalanced dataset. It contained a lot of missing values in numerical features which were correlated, Also the scaling was highly skewed. Categorical data contained a lot of low frequency categories. They wanted a final model performance on a test dataset on chosen KPIs (I chose F1-score).

Q1. Questions on the case study - assumptions made, why did you choose a particular KPI? What was the loss function? How did you deal with class imbalance, nan values , high number of categories? Did you perfo...read more
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Q2. Questions related to past experience - I told him about my last project which was based on computer vision. Interviewer asked a lot of clarifying questions and inquired about the process
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Q1. Asked about previous projects and the business impact.
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Q2. What challenges have you faced managing a team?
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Q3. Asked about a hypothetical scenario, how would you help the customer with that
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Q4. What would you do if someone in your team is not performing well
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Q1. Salary Discussion
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Q2. Previous employment history
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