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:
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.
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.
annual_payments table, write SQL queries to answer the following questions:
"paid"have an amount greater or equal to 100?
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
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.
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.
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
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.
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.
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?
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.
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?
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.
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?
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?
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:
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.
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.
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.
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?
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.
With a question like this, remember these tips:
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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