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JPMorgan Chase & Co. Data Scientist Interview Questions and Answers

Updated 10 Sep 2024

JPMorgan Chase & Co. Data Scientist Interview Experiences

2 interviews found

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Coding Test 

I was asked Python, sql, coding questions

Round 2 - Case Study 

Case study on how would you identify the total number of footfall on a airport

Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-

I applied via campus placement at Birla Institute of Technology and Science (BITS), Pilani

Round 1 - Technical 

(1 Question)

  • Q1. Work done in previous companty
  • Ans. 

    Developed machine learning models to predict customer churn and optimize marketing campaigns.

    • Built predictive models using Python and scikit-learn

    • Utilized SQL to extract and manipulate data for analysis

    • Collaborated with cross-functional teams to implement data-driven solutions

  • Answered by AI

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Interview questions from similar companies

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
No response

I applied via Referral and was interviewed in Nov 2024. There were 2 interview rounds.

Round 1 - Technical 

(4 Questions)

  • Q1. Types of Chunking in data preparation in RAG
  • Q2. How Embedding works in Vector Databases
  • Q3. Explain ARIMA model
  • Q4. How can we decide to choose Linear Regression for a business problem
Round 2 - Technical 

(4 Questions)

  • Q1. What is token and it's limit for Open Source LLMs
  • Q2. Difference of a Regression and Time Series problem
  • Q3. Advantage of LSTM over RNN
  • Q4. Performance Metrics for Logistic Regression

Data Scientist Interview Questions & Answers

Bajaj Finserv user image Vaibhav Diwakar Gavli

posted on 6 Jan 2025

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Aptitude Test 

SQL & aptitude question

Round 2 - Coding Test 

1 coding question for 45 min

Round 3 - Technical 

(1 Question)

  • Q1. Detailed questing for machine learning model's.
Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via LinkedIn and was interviewed in Jul 2024. There were 3 interview rounds.

Round 1 - Assignment 

Assignment on credit risk

Round 2 - Technical 

(1 Question)

  • Q1. Hyperparameter tuning
Round 3 - Technical 

(1 Question)

  • Q1. Case study for problem solving
Interview experience
5
Excellent
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(2 Questions)

  • 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

  • Answered by AI
  • 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

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
2-4 weeks
Result
Selected Selected

I applied via Company Website and was interviewed before Aug 2023. There were 2 interview rounds.

Round 1 - Technical 

(2 Questions)

  • 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...

  • Answered by AI
  • 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

  • Answered by AI
Round 2 - HR 

(2 Questions)

  • Q1. Basic questions
  • Q2. Strength weakness

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-

I was interviewed before Apr 2023.

Round 1 - Technical 

(1 Question)

  • Q1. Basic statistics
Round 2 - Technical 

(1 Question)

  • Q1. Project related

Interview Preparation Tips

Interview preparation tips for other job seekers - Donot join citi....no job security at all...I joined and was thrown in 3months due to their restructuring and budget issues.very bad management
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - Technical 

(1 Question)

  • Q1. Asked about ml algos
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(1 Question)

  • 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...

  • Answered by AI

Skills evaluated in this interview

JPMorgan Chase & Co. Interview FAQs

How many rounds are there in JPMorgan Chase & Co. Data Scientist interview?
JPMorgan Chase & Co. interview process usually has 1-2 rounds. The most common rounds in the JPMorgan Chase & Co. interview process are Coding Test, Case Study and Technical.
How to prepare for JPMorgan Chase & Co. Data Scientist interview?
Go through your CV in detail and study all the technologies mentioned in your CV. Prepare at least two technologies or languages in depth if you are appearing for a technical interview at JPMorgan Chase & Co.. The most common topics and skills that interviewers at JPMorgan Chase & Co. expect are Data Science, Machine Learning, Python, Artificial Intelligence and Data Analytics.

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JPMorgan Chase & Co. Data Scientist Interview Process

based on 2 interviews

Interview experience

5
  
Excellent
View more
JPMorgan Chase & Co. Data Scientist Salary
based on 95 salaries
₹10.8 L/yr - ₹42 L/yr
75% more than the average Data Scientist Salary in India
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JPMorgan Chase & Co. Data Scientist Reviews and Ratings

based on 7 reviews

4.7/5

Rating in categories

3.8

Skill development

4.5

Work-life balance

4.2

Salary

4.8

Job security

4.8

Company culture

3.9

Promotions

4.6

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