JPMorgan Chase & Co. Data Scientist Interview Questions + Guide in 2025

JP Morgan Chase & Co. is a leading global financial services firm and one of the largest banking institutions in the US. The company increasingly relies on data-driven decisions and sophisticated data management to support top clients. They require data scientists in areas such as Cybersecurity, Investment Banking, and Commercial Banking to tackle challenges like risk analysis, fraud investigation, and market research.

If you’re preparing for a Data Scientist position at JP Morgan Chase, this guide will help. It covers key topics like JPM data query techniques and JP Morgan Chase data scientist interview questions to boost your chances of success. Read on for insights to excel in your interview!

The JP Morgan data scientist interview process is rigorous, reflecting their commitment to hiring individuals who are not only technically adept but also aligned with the company’s values and long-term objectives. The process usually consists of four interview rounds but may differ based on the team and seniority of the position.

1. HireVue Interview

This round is often conducted over the HireVue platform. You will be asked to record a series of video responses and respond to coding challenges. This round aims to assess your candidature virtually to ensure you are a good fit for the in-person rounds later.

2. In-person Interview(s)

If it’s a good fit, you will be invited onsite to meet your team and have a panel interview. These rounds typically involve a mix of technical, behavioral, and case study questions.

Interview tips from JP Morgan’s careers page: “Stay current on the general and firm-specific news so you can speak from a place of knowledge and confidence. Be ready to share specific examples of your previous experience that reflect transferrable skills to the opportunity you are applying for. Prepare questions for our team so you can learn more about the opportunity and our firm.”

You will be expected to be technically sound in SQL, Python, machine learning algorithms, and analytical solutions. You will apply these technical skills to the company’s real-life scenarios, such as risk management, fraud detection, investment strategies, operational improvements, etc.

It is a good idea to stay updated about the company through its website and LinkedIn page and follow firm-specific. Data science-related news to stay abreast of the business problems you may encounter.

For a more in-depth discussion, look through our list below. We’ve hand-picked popular questions that were actually asked in JP Morgan’s Data Science interview.

1. Describe a challenging data science project you handled. How did you manage the complexities, and what was the outcome?

You’ll face a lot of complex decision-making at JP Morgan, so you must showcase your experience handling such situations.

How to Answer

Focus on a project you feel comfortable discussing in depth. Detail your approach, strategies, and impact. Be authentic, and make sure to demonstrate that you worked collaboratively with your team as well as stakeholders.

Example

“In my previous firm, I led a project to optimize investment strategies using machine learning. The challenge was integrating disparate data sources while ensuring model accuracy. My approach involved collaborating with cross-functional teams to refine data integration and iteratively improving the model based on stakeholder feedback. The outcome was a 15% improvement in prediction accuracy, significantly aiding our decision-making.”

2. Why do you want to join JP Morgan?

Interviewers will want to know why you specifically chose the Data Scientist role at JP Morgan. They want to establish whether you’re passionate about the company’s culture and values or your interest is temporary.

How to Answer

Your answer should cover why you chose the company and role and why you’re a good match for both. Frame your response positively. Additionally, focus on how your selection would benefit both parties.

Example

“J.P. Morgan promises the opportunity to work on complex financial challenges. This aligns with my passion for tackling intricate financial problems and my background in financial analysis and data-driven decision-making. My skills, coupled with my enthusiasm for innovation in finance, make me a good fit. The firm’s commitment to employee development and its inclusive culture also resonate with my professional values and aspirations.”

3. Tell us about a time when you had to explain complex data science concepts to non-technical stakeholders. How did you ensure they understood?

As you will be expected to participate in cross-functional teams and projects, the ability to communicate complex ideas effectively is non-negotiable.

How to Answer

Highlight your communication skills through a specific instance from a past project. Use the STAR method of storytelling - discuss the Specific situation you were challenged with, the Task you decided on, the Action you took, and the Result of your efforts.

Example

“I was tasked with explaining the outcomes of a predictive model to our marketing team in a past project. I used analogies related to their daily work to illustrate how the model functions and its relevance to their campaigns, avoiding any unnecessary technical jargon. I followed up with a Q&A session to address any doubts. This extra effort went a long way in promoting team dynamics and ensuring that the marketing team felt included in the technical conversations.”

4. How do you prioritize multiple deadlines?

You may need to work across teams, projects, and even geographies in a global organization like JP Morgan Chase. Time management and organization are essential skills to succeed.

How to Answer

Emphasize your ability to differentiate between urgent and important tasks. Mention any tools or frameworks you use for time management. It’s also important to showcase your ability to adjust priorities.

Example

“In a previous role, I often juggled multiple projects with tight deadlines. I prioritized tasks based on their impact and deadlines using a combination of the Eisenhower Matrix and Agile methodologies. I regularly reassessed priorities to accommodate changes and proactively communicated with stakeholders about progress and potential delays.”

5. Can you provide an example of a time when you had to make a quick decision based on incomplete data?

Real-world data is seldom perfect, and there will be occasions when your team or manager asks for your input when a quick decision is paramount. The interviewer wants to test your domain knowledge and critical thinking skills.

How to Answer

Provide an example where you had to make a timely decision with partial data. It’s important to convey the rationale behind your decision. You should also demonstrate that you are willing to seek expert help when needed - this shows that you are a team player.

Example

“We faced a tight deadline in my old firm to launch a marketing campaign with incomplete customer data. I looked at existing trends to extrapolate missing information and consulted with domain experts. Based on this, we made an informed decision to proceed with a targeted approach, which ultimately resulted in a successful campaign with higher-than-expected engagement rates.”

6. Given a list of tuples featuring names and grades on a test, write a function to normalize the values of the grades to a linear scale between 0 and 1.

You will need to demonstrate basic data manipulation problem skills in Python, as such operations are necessary for the day-to-day coding requirements for a Data Scientist in JPMC.

How to Answer

Briefly outline your approach, which should involve finding the minimum and maximum grades and then applying a formula to normalize each grade.

Example

“My approach would be to extract the grades from the list of tuples, find the minimum and maximum grades, and then normalize each grade using the formula: (grade - min_grade) / (max_grade - min_grade).”

7. You have access to two tables: transactions, which includes fields like transaction_id, customer_id, amount, and transaction_date, and customers, which includes customer_id, age, and income. Write an SQL query to identify the top 10% of customers by transaction volume in the last quarter and provide insights into their age and income distribution.

In a JP Morgan Data Scientist interview, a question like this will evaluate your ability to extract meaningful insights from financial data using SQL window functions.

How to Answer

Explain your SQL logic systematically. Discuss your insights and how they would aid business strategies.

Example

“I’d join the transactions table with the customers table on the customer_id column. Then, I’d filter the transactions to include those from the last quarter. Using a window function like RANK() or NTILE(), I’d identify the top 10% of customers based on transaction volume. Finally, I’d analyze the age and income distribution by looking for patterns that could inform targeted marketing or product development.”

8. You are given a deck of 500 cards numbered from 1 to 500. If the cards are shuffled randomly and you are asked to pick three, one at a time, what’s the probability of each subsequent card being larger than the previously drawn one?

Probability, permutations and combinations, and logical thinking are mathematical skills essential to analyzing financial data at JPMC.

How to Answer

Emphasize the importance of considering all possible combinations of three cards and then the favorable outcomes. Inform the interviewer what mathematical approach (Binomial distribution) you will follow.

Example

“The total number of ways to draw three cards from 500 is $^{500}C_3$. Each specific set of three cards can only be arranged in one way to meet the condition (ascending order). So, the probability is the number of sets of three cards, which is $^{500}C_3$ divided by the total number of ways to draw three cards.”

9. Explain how an XGBoost model differs from a Random Forest model.

You need to know about advanced machine learning techniques to solve complex problems such as credit risk in JPMC.

How to Answer Focus on the key differences and provide examples of potential applications in financial modeling.

Example

“XGBoost is a gradient boosting algorithm that builds trees one at a time, where each new tree helps to correct errors made by previously trained trees. It uses gradient descent to minimize loss when adding new models. On the other hand, Random Forest creates a ‘forest’ of decision trees trained on random subsets of data and averages their predictions. This parallel approach in Random Forest differs from the sequential tree-building in XGBoost. Also, XGBoost includes regularization, which helps in reducing overfitting.”

10. Write a function to calculate the total profit gained from investing in an index fund from the start to the end date.

You will be expected to know how to code functions related to investment scenarios on the fly, so be sure to practice such problems in Python.

How to Answer

Considering the discrete nature of share purchases and daily price changes, you need to calculate the total profit from transactions in an index fund. Mention the importance of accounting for the daily valuation of the fund and the timing of transactions.

Example

“I’d first track the total number of shares owned, updating it based on daily deposits and withdrawals. I’d calculate the share purchases based on the available funds and daily index price. For each day, I’d adjust the value of the holdings based on the index’s daily price change. This approach mirrors real-world scenarios at J.P. Morgan.”

11. In analyzing financial transaction data at JPMC, how would you differentiate and handle erroneous outliers versus those representing significant but valid market events?

Addressing how to handle outliers in transaction data demonstrates your analytical skills and domain expertise in financial data.

How to Answer

Emphasize the importance of understanding the context of the data. Differentiate between outliers by investigating their source: erroneous outliers often stem from data entry errors or technical glitches. In contrast, valid outliers could be due to significant market events like a merger or regulatory change. Stress the importance of using statistical methods to identify outliers, coupled with domain knowledge to interpret them.

Example

“I would use statistical methods like z-scores or IQR to identify outliers. Then, I’d investigate each outlier’s context. For example, if an outlier coincides with a major market event, like a central bank announcement, it’s likely a valid data point reflecting market reaction. However, it might be erroneous if the outlier deviates significantly from market trends without a corresponding event. I would consult with market experts or cross-reference with other data sources.”

12. Let’s say that you are working on analyzing salary data. You are tasked by your manager with computing the average salary of a Data Scientist using a recency-weighted average. Write the function to compute the average Data Scientist salary given a mapped linear recency weighting on the data.

Recency-weighted averages are an important statistical method to analyze trends where market rates fluctuate significantly.

How to Answer

Explain the concept and its relevance in data analysis. In your function, outline how you would assign greater weight to more recent salaries.

Example

“I would write a function that takes a list of salaries from the past ‘n’ years. The function will assign a linearly increasing weight to each year’s salary, with the most recent year having the highest weight. This approach ensures that recent trends in Data Scientist salaries significantly impact the computed average, reflecting the current market conditions more accurately.”

13. How would you estimate the valuation of the JP Morgan Chase mobile app?

This is a relevant situational exercise in applying finance and business analysis principles.

How to Answer

Start with the app’s direct financial impact, like revenue generation through transactions or cost savings. Then, consider the app’s strategic value, like customer retention, data collection, and brand enhancement. Use industry benchmarks and comparable analyses if possible.

Example

“I would first analyze its direct financial contributions, such as fees from mobile transactions or savings from reduced branch operations. Next, I’d assess the strategic value, like how the app improves customer engagement and retention, which can be quantified by looking at customer lifetime value. Additionally, I’d consider the value of data generated by the app for personalized marketing or risk assessment.”

14. Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Give an example of the tradeoffs between the two.

This tests your understanding of advanced machine learning techniques and their application in financial contexts, especially with complex datasets and in credit risk prediction.

How to Answer

Highlight the key differences and provide relevant examples of how you would employ each method.

Example

“Bagging, like in a Random Forest, is robust against overfitting and works well with complex datasets. However, it might not perform as well when the underlying model is overly simple. Boosting, exemplified by algorithms like XGBoost, often achieves higher accuracy but can be prone to overfitting, especially with noisy data. It’s also typically more computationally intensive.”

15. How would you address and rectify biases in a financial dataset?

Addressing biases in a financial dataset demonstrates your ability to ensure data integrity in relevant business scenarios.

How to Answer

Discuss the statistical techniques you would employ to detect anomalies and the need for thorough data cleaning. Mention the significance of using diverse datasets to train models and regularly updating them with new data to reduce bias over time.

Example

“In a financial context, dataset biases can lead to inaccurate models and unfair outcomes. To address this, I’d first conduct an exploratory data analysis to identify potential anomalies. For example, if we’re analyzing loan approval data, we must ensure it doesn’t inherently favor certain demographic groups. I’d use techniques like stratified sampling to ensure representative data and employ algorithms that are less susceptible to biases.”

16. Let’s say you are tasked with building a decision tree model to predict whether a borrower will repay a personal loan. How would you evaluate whether using a decision tree is the correct model? Let’s say you move forward with the decision tree model. How would you evaluate the model’s performance before deployment and after?

Evaluating and implementing a decision tree model for loan repayment prediction is a typical case study that emulates strategic challenges that JP Morgan is trying to solve.

How to Answer

Explain that decision trees are great for their simplicity and interpretability, which is crucial in banking for regulatory compliance and explainability. However, they can be prone to overfitting. Assess whether the dataset has features well-suited for a decision tree and if the model’s simplicity aligns with the complexity of the problem.

Example

“For evaluation, I’d focus on metrics like recall to minimize false negatives, as incorrectly predicting a default could be costly. Pre-deployment, I’d use a portion of the data to test the model, and post-deployment, I’d regularly compare the model’s predictions with actual loan outcomes, adjusting as necessary to ensure accuracy and fairness.”

17. What are the benefits of feature scaling in a logistic regression model?

This is asked to assess your understanding of data preprocessing and its impact on model accuracy and performance, which is crucial for data-driven financial decision-making.

How to Answer

Focus on how feature scaling aids in faster convergence during training, ensure uniformity in feature influence and enhances the interpretability of model coefficients. Talk about the practical implications of these benefits.

Example

“Feature scaling standardizes the range of independent variables, leading to faster convergence during optimization. For example, in a credit scoring model at JP Morgan, if income is in thousands and age is in years, income would disproportionately influence the model without scaling. By scaling, we ensure each feature contributes proportionally.”

18. We are considering creating a new partner card (think Starbucks-Chase credit card or Whole Foods-Chase credit card). You have access to all of our customer spending data. How would you determine what our next partner card should be?

This tests how you’d leverage analytics for strategic business decisions.

How to Answer

Discuss using customer spending data to identify trends and preferences. Elucidate the importance of clustering or segmentation techniques in understanding customer behavior.

Example

“I’d first analyze customer spending patterns by segmenting them based on spending categories, like groceries, dining, travel, etc. For example, if there’s a significant portion of customers with high spending in the hospitality sector, a hotel chain could be a suitable partner. Additionally, I’d look into the customer demographics and geographical data to ensure the chosen partner aligns with our customer base’s preferences and location.”

19. How would you explain Linear Regression to a non-technical person?

Data Scientists participate in cross-functional teams and projects at JP Morgan, so you need to have excellent communication skills and robust technical understanding.

How to Answer

Focus on explaining Linear Regression as a way to understand relationships between variables.

Example

“Imagine you’re looking at the relationship between the amount of time you spend studying and your exam scores. Linear Regression is essentially drawing a straight line through a set of points on a graph where each point represents a different amount of study time and the corresponding exam score. This line helps us predict, for example, what score you might expect if you studied for a certain number of hours.”

20. JP Morgan has begun a new email campaign. You are given 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?

Answering this well demonstrates your ability to apply Data Science to marketing effectiveness.

How to Answer

Focus on establishing a clear connection between email sent times and user site visits. Highlight the importance of A/B testing and control groups to isolate the effect of the emails.

Example

“I’d first link the timestamps of emails sent with users’ subsequent site visits. Compared to typical visit rates, a significant increase in visits shortly after emails are sent would indicate a positive impact. I’d also recommend an A/B test, where one group receives the emails, and another similar group doesn’t. Comparing these groups’ behaviors provides a clearer picture of the campaign’s effectiveness.”

21. Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order.

This question might be asked in a JP Morgan Data Scientist interview to assess a candidate’s ability to manipulate and process text data, which is essential for tasks like sentiment analysis, customer feedback analysis, and natural language processing in financial documents.

How to Answer

To parse them out of a string, we need to split the input string first. We would use the Python function .split() to create a list with each word as an input. Create another empty list that will eventually be filled with tuples.

Then, once we’ve identified each word, we need to loop through k-1 times (if k is the number of words in a sentence) and append the current word and subsequent word to make a tuple. This tuple gets added to a list that we eventually return.

Example

“Bigrams are pairs of consecutive words in a string useful in natural language processing. To find bigrams, I would start by splitting the string into words using Python’s .split() method and converting them to lowercase for consistency. Then, I would iterate through the list, forming bigrams by pairing each word with the next and storing these pairs as tuples in a list. Finally, I would return this list of bigrams. This approach demonstrates my understanding of both the concept and the practical implementation.”

22. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

This question might be asked in a JP Morgan Data Scientist interview to evaluate your problem-solving skills, attention to detail, and programming proficiency. It tests your ability to handle numerical data within a string, which is important for data cleaning and preprocessing tasks.

How to Answer

Start by iterating through each character in the string. Check if each character is a digit. If it is, convert it to an integer and add it to a running total. This approach allows you to ignore non-digit characters, such as the decimal point, and accumulate the sum of all digits efficiently.

Example

“To solve the problem of summing every digit in a string representing a floating-point number, I would iterate through each character in the string and check if it is a digit by seeing if it is in ‘0123456789’. If it is, I would convert it to an integer and add it to an accumulator variable. This method ensures that non-digit characters are ignored, and the final value of the accumulator will be the sum of all the digits in the string.”

Here are some tips to help you excel in your interview:

Study the Company and Role

Understand the basics of banking, investment, risk management, and the financial products JP Morgan deals with. Follow current trends in the finance industry and learn how data science is applied.

You can also read Interview Query members’ experiences on our discussion board for insider tips and first-hand information. For detailed information, visit JP Morgan’s page on their hiring process.

Understand the Fundamentals

Brush up on core Data Science topics like statistics, Machine Learning algorithms, data preprocessing, and model evaluation. Be comfortable with Python or R, SQL, and the libraries commonly used for Machine Learning and statistical modeling, like pandas, sci-kit-learn, and TensorFlow.

For further practice, refer to our popular guide on quantitative interview questions, or practice some cool fintech projects in machine learning to bolster your resume.

For further guidance, we also have a tailored Data Science Learning Path covering core topics and practical applications.

Prepare Behavioral Interview Answers

Soft skills such as collaboration and adaptability are paramount to succeeding in any job, especially Data Science roles where you’ll need to coordinate with teams from non-technical backgrounds and stakeholders from different geographies.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.

What is the average salary for a Data Science role at JP Morgan?

The average base salary for a Data Scientist at JP Morgan is US$128,435, making the remuneration competitive for prospective applicants.

For more insights into the salary range of Data Scientists at various companies, check out our comprehensive Data Scientist Salary Guide. You may also check out the top data science companies here.

Where can I read more discussion posts on the JP Morgan Data Science role in Interview Query?

Here is our discussion board where Interview Query members talk about their JP Morgan interview experience. You can also use the search bar to look up the general Data Science interview experience to gain insights into other companies’ interview patterns.

Are there job postings for JP Morgan Data Science roles on Interview Query?

We have jobs listed for Data Science roles in JP Morgan, which you can apply for directly through our job portal. You can also have a look at similar roles that are relevant to your career goals and skill set.

In conclusion, succeeding in a JP Morgan Data Scientist interview requires a strong foundation in coding and algorithms, applying them to real-world financial problems, and communicating your findings to business stakeholders.

If you’re considering opportunities at other companies, check out our Company Interview Guides. We cover a range of similar companies, so if you are looking for Data Science positions in financial or banking firms, you can check our guides for Citi, Morgan Stanley, Wells Fargo, and more.

For other data-related roles at JP Morgan, consider exploring our Business Analyst, Machine Learning Engineer, Product Analyst, and similar guides in our main JP Morgan Chase interview guide.

We hope you’ll land your dream role very soon!