C1x Adtech Lead Data Scientist Interview Questions and Answers

NER training using deep learning

Q1. Describe the approach taken for assignment
Ans. 
I approach assignments by breaking them down into smaller tasks, setting deadlines, and regularly checking progress.
• Break down the assignment into smaller tasks to make it more manageable

• Set deadlines for each task to stay on track

• Regularly check progress to ensure everything is on schedule

• Seek feedback from colleagues or supervisors to improve the quality of work
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Q2. Scenario based questions
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They gave a span of 3 days to build an AI-powered webapp

Q1. How would you go about learning a new skill
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Q2. Experience in cloud technologies
Ans. 
I have experience working with cloud technologies such as AWS, Azure, and Google Cloud Platform.
• Experience in setting up and managing virtual machines, storage, and networking in cloud environments

• Knowledge of cloud services like EC2, S3, RDS, and Lambda

• Experience with cloud-based data processing and analytics tools like AWS Glue and Google BigQuery
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Q1. Tell me about yourself
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Q2. Project details and challenges faced in the project
Ans. 
Developed a predictive model for customer churn in a telecom company
• Collected and cleaned customer data from various sources

• Performed exploratory data analysis to identify key factors influencing churn

• Built and fine-tuned machine learning models to predict customer churn

• Challenges included imbalanced data, feature engineering, and model interpretability
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Q1. Ml based questions, linear regression, classifications, regularizations, clustering based questions
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Q2. Coding test based on a data frame
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Q1. Techno managerial round
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Q2. Lots of ML use case based questions
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Q1. Interview with Director
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Q2. Questions based on projects done on cv
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Q1. Tell me about your project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company
• Used Python and scikit-learn for data preprocessing and model building

• Performed feature engineering to improve model performance

• Evaluated model using metrics like accuracy, precision, and recall

• Implemented the model in a production environment for real-time predictions
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1 hour, overall data science related, codility

Q1. What is data science?
Ans. 
Data science is a field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
• Data science involves collecting, analyzing, and interpreting large amounts of data to solve complex problems.

• It combines statistics, machine learning, data visualization, and computer science to uncover patterns and trends in data.

• Data scientists use programming language...read more
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Q2. Simple answer to this question
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Q1. Basics about regression models
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Q2. Basics of PCA, Boosting, bagging
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Q3. People Management
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Q4. Project information from resume
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Q1. Basic questions in python
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Q2. Questions in Ml
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Q1. Cross validation
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Q2. How to handle imbalanced dataset
Ans. 
Handling imbalanced datasets involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Utilize algorithms that are robust to imbalanced datasets, such as Random Forest, XGBoost, or SVM.

• Adjust class weights in the model to give more importance to the minority class.

• Use techniques like SMO...read more
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