JPMorgan Chase & Co. Lead Data Scientist Interview Questions and Answers

SQL & aptitude question

1 coding question for 45 min

Q1. Detailed questing for machine learning model's.
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Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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Q1. How do you define model Gini?
Ans. 
Model Gini is a measure of statistical dispersion used to evaluate the performance of classification models.
• Model Gini is calculated as twice the area between the ROC curve and the diagonal line (random model).

• It ranges from 0 (worst model) to 1 (best model), with higher values indicating better model performance.

• A Gini coefficient of 0.5 indicates a model that is no better than random guessing.

• Commonly used in credit
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Q2. How to you train XG boost model
Ans. 
XGBoost model is trained by specifying parameters, splitting data into training and validation sets, fitting the model, and tuning hyperparameters.
• Specify parameters for XGBoost model such as learning rate, max depth, and number of trees

• Split data into training and validation sets using train_test_split function

• Fit the XGBoost model on training data using fit method

• Tune hyperparameters using techniques like grid search
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Q1. How much experience do you have?
Ans. 
I have 8 years of experience in data science, with a focus on machine learning and predictive modeling.
• 8 years of experience in data science

• Specialize in machine learning and predictive modeling

• Worked on various projects involving big data analysis

• Experience with programming languages such as Python and R
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Q2. What is the tech stake you ahve worked on?
Ans. 
I have worked on developing machine learning models for predictive maintenance in the manufacturing industry.
• Developed machine learning algorithms to predict equipment failures in advance

• Utilized sensor data and historical maintenance records to train models

• Implemented predictive maintenance solutions to reduce downtime and maintenance costs
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Q1. What is Bert and transformer
Ans. 
Bert and transformer are models used in natural language processing for tasks like text classification and language generation.
• Bert (Bidirectional Encoder Representations from Transformers) is a transformer-based model developed by Google for NLP tasks.

• Transformer is a deep learning model architecture that uses self-attention mechanisms to process sequential data like text.

• Both Bert and transformer have been widely use...read more
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Q2. NLP pre processing techniques
Ans. 
NLP pre processing techniques involve cleaning and preparing text data for analysis.
• Tokenization: breaking text into words or sentences

• Stopword removal: removing common words that do not add meaning

• Lemmatization: reducing words to their base form

• Normalization: converting text to lowercase

• Removing special characters and punctuation
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Q1. Basic questions
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Q2. Strength weakness
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Q1. Asked about ml algos
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Q1. What is cross validation ?
Ans. 
Cross validation is a technique used to assess the performance of a predictive model by splitting the data into training and testing sets multiple times.
• Cross validation helps to evaluate how well a model generalizes to new data.

• It involves splitting the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset.

• Common types of cross validation include k-fold cross validation and lea...read more
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Python coding question and ML question

Q1. ML questions from resume + general
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Q1. Techno managerial round
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