JP Morgan Chase Data Scientist Interview Guide

JPMorgan Chase & Co. is a leading global financial services firm and one of the largest banking institutions in the U.S. Their operations cater to a broad spectrum of clients, including consumers, small businesses, corporations, governments, and institutions.

The company has a dedicated ‘Payments’ organization that focuses on delivering innovative payment services, holding simplicity, consistency, and control in high regard. Here are some of the questions asked in JP Morgan Chase & Co. data scientist interviews.

JPMorgan Chase & Co.’s interview process is tailored to assess a candidate’s proficiency in relevant technical areas, problem-solving skills, and alignment with the company’s values and work culture. This multi-phased process is structured as follows:

1. Initial Screening: The process begins with an initial screening conducted by a recruiter to understand the candidate’s background, experiences, and potential fit for the role. This stage may include a review of the candidate’s resume and a discussion about their career interests and goals.
2. Technical Assessments: Candidates may undergo technical assessments, which can include coding challenges, SQL tests, and other relevant technical evaluations based on the role they are applying for. These assessments gauge the depth of a candidate’s knowledge in critical areas necessary for the position.
3. Behavioral Interviews: In this stage, candidates are assessed for their problem-solving ability, teamwork, leadership skills, and cultural fit within JPMorgan Chase & Co. These interviews often entail discussing past experiences and how they handled different situations in their previous roles.
4. Data Science Case Study Interviews: Depending on the role, candidates may be presented with case studies relevant to the position they are applying for. This allows the interviewers to evaluate how candidates approach real-world problems, analyze situations, and propose solutions.
5. Onsite Interviews: Successful candidates are then invited for onsite interviews where they engage in multiple back-to-back sessions with different team members. These sessions could include further technical evaluations, discussions with potential team members, and additional behavioral or situational questions.
6. Team Matching: Once a candidate successfully clears the interview stages, a team-matching process is undertaken to find the most suitable team and role for the candidate within JPMorgan Chase & Co. This stage ensures that the candidate is placed in a role that aligns with their skills, interests, and the needs of the business.
7. Offer and Onboarding: Upon successful matching, an offer is extended to the candidate. Once accepted, the onboarding process begins to integrate the candidate into JPMorgan Chase & Co.’s work environment and culture.

JPMorgan Chase & Co.’s interview process is rigorous and thorough, reflecting the institution’s commitment to hiring individuals who are not only technically adept but also aligned with the company’s values and long-term objectives.

JPMorgan Chase & Co. is a leading global financial institution with a substantial presence in the banking sector. With a vast amount of data handled within the organization, having a strong foundation in database management and SQL is crucial for aspiring candidates.

JPMorgan Chase & Co. has adopted a comprehensive data structure based on the concept of “data products” and utilizes cloud-based database engines, including Amazon Athena, Amazon Redshift Spectrum, and Amazon EMR, for data querying and processing alongside its on-premises data lakes managed with Hadoop. The sections below present a variety of database-related questions that may be encountered during interviews at JPMorgan Chase & Co.

1. Write a query to find all neighborhoods with no users.

We’re given two tables, a users table with demographic information and the neighborhood they live in, and a neighborhoods table. Write a query that returns all neighborhoods that have 0 users.

2. Create SQL queries to answer questions about transactions in the annual_payments table.

Given an annual_payments table, write SQL queries to answer the following questions:

1. How many total transactions are in this table?
2. How many different users made transactions?
3. How many transactions listed as "paid" have an amount greater or equal to 100?
4. Which product made the highest revenue? (use only transactions with a "paid" status)

3. Select all entries from the flights table.

Assuming you work for an airline and the flights table contains information about all flights your airline has booked, write a query to select all entries from the flights table.

For practicing SQL, consider the SQL learning path and the full list of SQL questions and solutions in our interview questions database.

With a robust data environment that encompasses cloud-native and on-premises solutions, candidates are expected to demonstrate proficiency in navigating complex datasets and crafting efficient algorithms. Here are some representative questions that delve into text analysis and efficient data handling, which are essential skills within a financial institution.

4. Write a Python function to find all bigrams in a given sentence or paragraph.

You are given a sentence or paragraph of strings. Your task is to write a function called find_bigrams that returns a list of all its bigrams in order.

5. Write a function to simulate the event of drawing balls from a bin.

Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

6. Write a Python script to count the total number of lines in a 100 GB log file.

You are given a huge 100 GB log file. Your task is to write a Python script that counts the total number of lines in the file.

To practice Coding and Algorithms interview questions, consider using the Python learning path or the full list of Coding and Algorithms questions in our database.

Machine learning is integral to data-driven decision-making at JPMorgan Chase & Co. The institution heavily invests in artificial intelligence and machine learning to derive actionable insights from its vast data reservoirs.

For roles that delve into machine learning, understanding various algorithms and their application in the financial domain is critical. Here, we present a selection of machine learning-related questions that candidates may come across during interviews at JPMorgan Chase & Co.

7. How would you compare two credit risk models and measure the success of a new model?

Your co-worker develops a model that determines loan eligibility. Another co-worker believes they have a better model for predicting loan defaults. Given that personal loans are monthly installments, how would you compare the two models over time? What metrics would you track to measure the success of the new model?

8. When would you choose a bagging algorithm over a boosting algorithm in machine learning?

We’re comparing two machine learning algorithms. In what scenario would you prefer a bagging algorithm to a boosting algorithm? Provide an example of the trade-offs between the two.

9. How would you evaluate the use and performance of a decision tree model for predicting loan repayment?

You’re tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you assess if a decision tree algorithm is the right model for this problem? If you proceed with the decision tree model, how would you evaluate its performance before and after deployment?

To prepare for machine learning interview questions, consider using the machine learning learning path. These resources will help you understand and solve complex machine learning problems.

Analytics and experiments are crucial in steering decisions and strategies at JPMorgan Chase & Co. The organization employs a variety of technologies, including AWS Glue, AWS Lake Formation, and Microsoft Power BI, to manage, share, and analyze data securely and efficiently.

As such, analytical skills paired with an understanding of data management principles are highly valued. Below are some representative questions related to analytics and experiments that reflect the organization’s focus on data-driven strategies.

10. How would you determine if the redesigned email campaign led to an increase in conversion rates?

You work for an E-commerce store where the new-user-to-customer conversion rate increased from 40% to 43% after the new marketing manager redesigned the email journey. However, the conversion rate was 45% a few months before the new manager started and then dropped to 40%. How would you investigate if the redesigned email campaign actually led to the increase in the conversion rate and that the increase wasn’t instead the result of other factors?

11. How would you analyze transaction data to identify the cause of a reduction in revenue?

An e-commerce company has been experiencing a reduction in revenue for the past 12 months. You have transaction data, including the date of sale, the total amount paid by the customer, profit margin per unit, quantity of item, item category and subcategory, marketing attribution source, and discount applied. How would you analyze this dataset to understand exactly where the revenue loss is occurring?

12. How would you measure the success of a new email campaign and analyze it using a SQL query?

Your company has started a new email campaign. You have tables detailing users’ visits to the site and timestamps of when emails were sent to users. How would you measure the success of this campaign and write a SQL query to analyze its success?

For practicing Analytics and Experiments, consider using the product metrics learning path and the data analytics learning path. These resources will help you understand and solve complex problems in this field.

Statistics and probability form the backbone of data analysis and decision-making processes at JPMorgan. Proficiency in these domains is crucial for roles that require a deep understanding of data trends and patterns, especially in risk management and financial forecasting.

Here, we present a selection of statistics and probability-related questions to help prepare candidates for interviews at this esteemed financial institution:

13. What’s the probability of each subsequent card being larger than the previous one in a shuffled deck of 500 cards?

Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly, and you are asked to pick three cards, one at a time, calculate the probability of each subsequent card being larger than the previously drawn card.

14. What is the probability of a biased coin landing as heads exactly 5 times out of 6 tosses?

Assume you have a biased coin that comes up heads 30% of the time when tossed. Calculate the probability of the coin landing as heads exactly 5 times out of 6 tosses.

15. What is the probability that the median of three random variables is greater than 3?

Given three random variables independently and identically distributed from a uniform distribution of 0 to 4, calculate the probability that the median of them is greater than 3.

For practicing Statistics and Probability, consider using the Statistics and A/B testing learning path and the Probability learning path. These resources will help you understand and solve complex problems in these areas.

Behavioral interviews are almost always a necessary part of every job interview. At JPMorgan Chase and Co., interviewees are asked behavioral interview questions to assess their culture fit and how their ideals fit company goals. Here are some of the common behavioral interview questions you might encounter.

16. How would you lead a complex project?

Can you describe a complex project that you led? Please detail the challenges, the steps you took to overcome them, and the final outcome. What was the problem you were solving, and how did the project’s success impact your organization?

17. Tell me about a time when you exceeded expectations during a project. What did you do, and how did you accomplish it?

This question warrants a classic STAR method response. The only difference to the usual response is that you need to specifically state a situation where you did more than what was required of you. Do not overthink this. It could be as simple as adding visualizations to a presentation when a boss did not specifically ask for them, or adding logic to handle an edge case that no one had previously identified. Be sure to focus on the value added by your additional effort.

18. Describe a time when you were going to miss a deadline. How did you respond?

With a question like this, remember these tips:

• Describe the scenario that led to the missed deadline.
• Avoid talking about what others did that put the project behind schedule.
• Be honest about what went wrong and what you learned.

You might say:

“I was in charge of creating an important data analytics report in my previous role. Due to an ETL error, the data we were using for the project the data wasn’t available. As the deadline approached, I knew the report wouldn’t be finished, so I informed my manager about the issue, provided a revised timeline for when it would be done, and worked with the data engineering team to fix the ETL error.”

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