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We only focus on making you a clear interview with ease. We have released our Become a Full Stack Analytics Professional for anyone in 2nd year of graduation to 8-10 YOE. This book contains 23 topics and each topic is divided into 50/100/200/250 questions and answers. Pick the book and read it thrice, learn it, and appear in the interview.
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Day 26 – JPMorgan Chase Data Analyst Interview

Company: JPMorgan Chase
Designation: Data Analyst
Year of Experience Required: 2 to 6 years
Technical Expertise: SQL, Python/R, Statistics, Machine Learning, Case Studies
Salary Range: 20 LPA – 45 LPA

JPMorgan Chase is a global leader in financial services, offering solutions in investment banking, asset management, and commercial banking. Known for its data-driven decision-making, JPMorgan Chase relies on skilled Data Analysts to manage and analyze vast amounts of financial data. If you’re preparing for a Data Analyst role at JPMorgan Chase, here’s a detailed breakdown of their interview process and the questions you can expect.

JPMorgan Chase Data Analyst Interview

These questions will enhance your knowledge and help you to discover your weaknesses in various topics.

Round details are below

Round 1 – Telephonic Screening Round

Focus: Basic understanding of Data Analysis concepts, SQL, and Python/R.
Format: Discuss your resume, past projects, and solve introductory coding/SQL problems.

Round 2 – Face-to-Face Technical Round

Focus: Advanced Statistics and foundational SQL.
Format: Solve problems on a whiteboard or shared document (e.g., hypothesis testing, query optimization).

Round 3 – Project Analysis

Format: Explain your approach, tools, challenges, and the impact of your work.
Focus: Deep dive into your past projects.

Round 4 – Case Studies

Focus: Real-world business problems (e.g., risk analysis, customer segmentation).
Format: Propose data-driven solutions and defend your strategy.

Round 5 – Hiring Manager Round

Focus: Cultural fit, communication skills, and alignment with JPMorgan Chase’s goals.
Format: Behavioral questions and discussions about long-term career aspirations.

Difficulty of Questions

SQL – 8/10

1) Write a suitable query where you will collect the names of visitors in a conference who have less than 8 characters in their name. Also, separate the ones with a middle name.
(Hint- You can take appropriate column_names)

To find visitor names with fewer than 8 characters and separate those with a middle name.

SELECT name  
FROM visitors  
WHERE LENGTH(name) < 8;  
SELECT name  
FROM visitors  
WHERE name LIKE '% % %';

2) How Do Multiple Triggers Execute in SQL?

If multiple triggers exist on the same event (e.g., BEFORE INSERT, AFTER UPDATE), their execution order depends on the database system:

• MySQL, triggers execute in the order they were created.
• SQL Server, triggers execute in an undefined order, but you can control their execution using sp_settriggerorder.
• PostgreSQL, the order is unpredictable, so defining logic in separate triggers is recommended.

If you have multiple triggers for the same action, it’s best to manage their sequence manually by using procedural logic inside a single trigger.

3) How to Fetch Alternate Records (Even Rows) from a Table?

To retrieve even-numbered rows, we can use the MOD() function:

SELECT * FROM Employee 
WHERE MOD(empid, 2) = 0;

This query selects only rows where empid is even. If there’s no numeric ID, we can use ROW_NUMBER():

SELECT * FROM (
    SELECT *, ROW_NUMBER() OVER (ORDER BY empid) AS row_num FROM Employee
) temp WHERE MOD(row_num, 2) = 0;

4) What is the Use of the FETCH Command?

The FETCH command is used to limit the number of rows returned in a query. It is commonly used with OFFSET for pagination.

Example: Get the first 5 employees from the Employee table:
SELECT * FROM Employees ORDER BY empid FETCH FIRST 5 ROWS ONLY;

5) Write a Query to Get All Students with Name Length 10, Starting with ‘K’ and Ending with ‘z’.

SELECT * FROM Student  
WHERE LENGTH(name) = 10  
AND name LIKE ‘K%z’;

R/Python – 7/10

1) Write a Python function to reverse a given string.

2) Write a Python function to check if a given string is a palindrome (reads the same forwards and backward). Ignore case and spaces.

3) Find the Largest Number in a List

4) Write a Python function to count the occurrences of each word in a given string.

5) Write a Python function to check if a given number is prime.

Statistics/ML – 9/10

1) Give an example where the median is a better measure than the mean

The median is better than the mean when dealing with skewed data or outliers.
Example: Suppose we analyze salaries in a company:

• 9 employees earn $50,000 each.
• The CEO earns $5,000,000.

The mean salary is:
(9 * 50,000 + 5,000,000) / 10 = (450,000 + 5,000,000) / 10 = 5,450,000 / 10 = 545,000
The median salary is $50,000.

Since the mean is heavily affected by the CEO’s high salary, the median gives a better representation of a typical employee’s salary.

2) What is an example of a data set with a non-Gaussian distribution?

A non-Gaussian distribution does not follow the bell curve.
Example:

• Income Distribution: Most people earn lower wages, while a few earn extremely high salaries, creating a right-skewed distribution.
• Website Traffic: A few pages get millions of views, while most get very few, following a power law distribution.

3) A model experiencing Underfitting has poor predictive performance. Suggest a method to ensure that the model is free from Underfitting.

Underfitting occurs when a model is too simple and fails to capture patterns in the data.
Solutions:

• Use a more complex model, like moving from linear regression to polynomial regression.
• Increase the number of features to provide more information.
• Reduce regularization strength (e.g., decrease L1/L2 penalties in models like logistic regression).
• Train for more epochs in neural networks.

4) Why is dimension reduction important?

Dimension reduction improves performance, efficiency, and interpretability in machine learning.
Benefits:

• Removes noise and avoids overfitting.
• Speeds up computations by reducing feature space.
• Improves model accuracy by removing irrelevant features.
• Helps in visualization (e.g., using PCA to reduce data to 2D or 3D).

Example:
If a dataset has 1000 features, many may be redundant. Principal Component Analysis (PCA) can reduce it to 50 key features without much loss of information.

5) What is the Central Limit Theorem? Explain it. Why is it important?

The Central Limit Theorem (CLT) states that:

• The sampling distribution of the mean of any dataset (with finite variance) will approximate a normal distribution as the sample size increases, regardless of the original distribution.

Why is it important?

• Allows us to use normal distribution-based statistical tests even when the data isn’t normally distributed.
• Helps estimate population parameters from sample statistics.
• Forms the foundation of confidence intervals and hypothesis testing.

Example: If we sample 100 people’s incomes, the individual incomes may be skewed, but the sample mean will follow a normal distribution if we take many samples.

Case Study

Problem Statement:
JPMorgan Chase has noticed fluctuations in customer spending and wants to identify key insights from transactional data. As a data analyst, you are provided with a dataset containing customer transactions over the past year. Your goal is to analyze customer spending behavior, detect anomalies, and suggest data-driven recommendations.

Dataset Overview:

You are given a dataset with the following columns:

• Payment_Method – Payment type (credit card, debit card, online banking, etc.)
• Transaction_ID – Unique identifier for each transaction
• Customer_ID – Unique identifier for each customer
• Transaction_Date – Date of the transaction
• Transaction_Amount – Amount spent in the transaction
• Merchant_Category – Category of the merchant (e.g., groceries, travel, dining, entertainment)

Key Questions to Answer:

1. Identify the top 10% of high-value customers based on spending.

• How do you define a high-value customer?
• What is the total spending of each customer?
• What percentage of total revenue comes from the top 10% of spenders?

2. Analyze spending trends over time.

• What are the peak months for customer spending?
• How does spending behavior change during holidays or special events?
• Are there any seasonal trends in different merchant categories?

3. Detect unusual transactions or spending patterns.

• How would you identify fraudulent or unusual transactions?
• What spending patterns are common among most users, and what deviations should be flagged?

4. Category-wise spending analysis.

• What are the most popular spending categories?
• Do certain customer segments prefer specific categories (e.g., frequent travelers, food lovers)?

5. Payment method analysis.

• Which payment method is used the most?
• Are high-value customers more likely to use a specific payment method?

Key Insights & Business Recommendations

1. Identifying High-Value Customers

The top 10% of customers contribute a significant portion of total revenue. JPMorgan Chase can offer them personalized rewards, exclusive benefits, and tailored financial products to retain them.

2. Seasonal Trends in Spending

Spending increases during the holiday season, especially in December. The bank can use this insight to launch targeted promotions and cashback offers during peak shopping periods.

3. Detecting Unusual Transactions

Transactions that are significantly higher than the usual spending pattern should be flagged for potential fraud. Implementing automated alerts can enhance security.

4. Popular Spending Categories

Grocery and travel categories have the highest transactions. JPMorgan Chase can collaborate with top merchants to provide better deals and increase customer engagement.

5. Payment Method Preferences

If most customers prefer using credit cards, the bank can promote new credit card offers, rewards programs, and installment plans to increase customer satisfaction and spending.

you can practice a lot of case studies and other statistics topics here https://thedatamonk.com/data-science-resources/