JP Morgan Chase Data Scientist Interview Guide

tl;dr

JP Morgan Chase is one of the largest banks in the world, with $4.36 trillion in assets. As a substantial institution with global reach, they engage in a wide range of activities, including asset trading, commercial financing, and private banking services, among other financial and technical offerings. They currently employ over 317,000 people across all departments, and are based in New York City with offices in major cities all over the world.

In recent years, JP Morgan Chase has taken a major interest in burgeoning and established fintech companies, as well as the Web3 digital assets marketplace. Their latest efforts have focused on AI and machine learning, and they have invested heavily in building teams of developers and researchers in order to integrate these tools into everything from contract negotiations to data modeling. They have strategically positioned these teams to circulate the tools they develop internally, allowing every department at JP Morgan Chase to harness the benefits of their efforts.

JP Morgan Chase has leveraged their resources to create tools on the cutting edge of data analysis, large language models (LLMs), and other emerging technologies. They have also adopted hiring and team management approaches that mirror those of established tech companies and startups, moving fast and engaging in frequent experimentation and iteration. Data scientists, developers, and others working in this capacity are expected to be skilled and performant, with less of an emphasis on culture fit. If you’re looking for a role that allows you to work on, test, and distribute the next generation of fintech tools, JP Morgan Chase might be ideal for you.

What does a JP Morgan Chase Data Scientist do?

The role is team-dependent, so you’ll have different responsibilities depending on the specific project, tools, and problems you work on. Generally speaking, you’ll use data analysis, ETL processes, data modeling, and other DS techniques to provide new and novel solutions to persistent organizational problems. You might also use your skills in collaboration with machine learning and software engineers to measure product performance, identify issues and chokepoints, and use data to inform high-level organizational decisions.

As JP Morgan Chase is an established company, data scientists typically work with a certain amount of structure, depending on the project. For example, you may work in sprints and use JIRA tickets, alongside other established enterprise processes. However, some data teams act like a “startup within the company,” employing less formal work processes. Examples of teams you might join:

• Research Technologies
• AI Research
• Machine Learning Center of Excellence (MLCoE)
• Focused Analytics Solutions Tea (FAST)
• Data Science
• Quantitative Finance

How you apply your DS skills will vary. For example, if you’re working on the quantitative finance team, you may be asked to build and test automated asset trading tools for JP Morgan Chase’s client, or work on a framework for risk assessment and management in collaboration with traders, market experts, and other stakeholders. You may also work on one of their proprietary teams—such as FAST or MLCoE, using machine learning techniques like reinforcement learning and language processing—to quickly deliver automated insights to clients and internal teams.

Working successfully on any of these teams will require you to be well-informed about DS fundamentals. You’ll also need to be able to defend your work and ideas, explaining your approach to fellow team members and other stakeholders in the organization who may not have a DS or technical background.

Before you apply

• Research JP Morgan Chase’s data science teams: As a role that could potentially build tools for huge parts of or even the whole organization to use, it’s important that you have a grasp of why JP Morgan Chase is investing in data science, machine learning, and other technologies. This is especially important if you don’t have a background working in fintech or for a large, established financial services firm, as the problems you are solving may be relatively new and require a certain amount of institutional know-how. Familiarize yourself with banking and finance terminology too, so you can confidently speak to common issues in finance.
• Brush up on DS basics and do practice problems: The interview process at JP Morgan Chase is long, and you’ll be required to speak about both complex and basic data science concepts, such as Data Structure Analysis (DSA), hash maps, and other basics. While you may not use these as much on the job, it’s important you show your ability to grasp the reasons and theories behind them.
• Get feedback from big tech DS interviewers will help you identify key areas you need to improve on and give you the skills to highlight your strengths, allowing you to tackle each round with confidence.

Interview process

The DS interview process at JP Morgan Chase is typically informal, and most of the interviewers will have limited to no direct interview training. As a result, many of the interviews will focus on practical skills and problems, or be a less structured conversational interview to assess your domain knowledge. In addition, the subjects may change as you go through the process and the team members identify areas they want to explore more deeply with you. The process typically includes 6 conversations:

1. Recruiter screening conducted remotely via phone or video call
2. Hiring manager interview conducted remotely via a video call, with technical elements done through a screen share
3. Coding and skills screening conducted remotely via a video call and screen share with VSCode or another development environment
4. Product questions and system design screening focused on areas relevant to the fintech space and to the work the team is doing
5. Open-ended skills assessment, which piggybacks off of your system design round, focusing on another skill like ML tool design and deployment
6. ML concepts interview, which is a more conversational round focused on your knowledge of the topic, past projects, and communication skills

Recruiter screening

This interview will take place remotely, and will focus on your recent experience, relevant academic background, and your approach to work and collaboration. You’ll need to describe your skills and experience in a way that maps well onto the skillset of the role you’re applying to; for example, talking about your work building and deploying ML algorithms if you’re applying to the MLCoE.

Some questions they may ask include:

Technical screening rounds

Hiring manager screening

This will be similar to the recruiter screening in some ways, but with a much more in-depth technical discussion added on. You’ll be asked about technical projects you've worked on, and about the concepts of tools you employed in those projects. If they ask you to do any technical work, it will likely be fairly straightforward, as this interview is more focused on assessing your ability to describe in detail the tools and techniques you have used.

Some example questions you might hear are:

• How would you use the groupby function in Pandas?

Coding and skills screening

This round will be conducted using screen share, allowing you to use whatever integrated development environment you prefer on your own computer. You will be given 1–2 coding questions to be completed either in SQL or Python. Because the interviewers are given a lot of leeway to select questions, you might be asked a general data science question or something quite specific to the team you’re applying to. For the latter, the interviewer may not expect you to provide a perfect answer, but rather show your thought process and come up with a novel approach to the problem.

Some example questions you might see in this round are:

• Find the maximum subarray sum.
• Find the longest substring without repeating characters.
• Given an array of strings, group together the words that are anagrams.
• Chase is launching a new credit card. Estimate how many signups you’ll have in the first month.

Product questions and system design

This round of questions is also team-dependent, but having some institutional and industry knowledge can help you exceed expectations. As a financial institution, JP Morgan Chase values optimizing for security, compliance, and reliability. Even if you aren’t personally experienced with fintech and banking compliance standards, showing some awareness of these requirements in your design process and speaking to them can help you stand out.

Many of the questions will draw from something the team does or is hoping to do; for example, designing a system to extract entities such as companies, people, or events from news stories in real time. This task, which you would encounter when applying to an AI-focused role, requires you to architect a system that incorporates both machine learning on the extraction side, and data processing, writing, and analyzing on the storage side.

Some other system design challenges you might see include:

• Build a news recommendation system in real time using AWS.
• Build an API and deploy it assuming end users are going to access the API 250,000 times per day.

System design and skills deep dive

To learn more about your process, this interview will bring in another team member who will be asked to obtain more insight into your skills, either because they want to test a particular segment of your skills or to further examine your ability to design performant systems. As such, this will likely piggyback off of one of the tasks you’ve already worked on, either in the system design or coding skills rounds. This round can also be dependent on the interviewer; for example, if you speak with someone who specializes in software development or data modeling, you may be tasked with solving a problem in those topic areas.

Similar to the system design round, you might be asked to optimize your entity extraction system to prioritize news stories and entities based on a set of characteristics in order to not be rate-limited when calling an AI API. Because you have a set limit of API calls you can hit the AI service with, you’ll need to design a way to “tier” news stories, prioritizing the most relevant stories for end users and ensuring those sorts of stories, topics, and entities are processed ahead of the others.

ML and AI concepts

The final round will be mostly conversational, with some light design and implementation discussions and mapping, depending on the questions the interviewer chooses to ask. This round will be focused on assessing your understanding of the fundamental elements of machine learning and AI tools, data structures, statistical analysis, and other key data science concepts. This round will also be somewhat more culture-focused, and involve discussion of your past projects, particularly those that relate to machine learning and AI.

The interviewer will ask you to explain a process, such as creating and deploying a machine learning tool to production. Again, it may be less formal and more conversational; for example, an open-ended discussion of the common use cases for machine learning tools in banking and finance, and what new tools could be in the works. In this case, a discussion about the way machine learning is changing contract writing and negotiation could lead you to a deeper dive into how you might design a tool to automate contract writing, or to extract and summarize key parts of a document.

Some topics you might cover in this interview are:

Additional resources

• JP Morgan Chase Data Science, Applied AI & Machine Learning page
• JP Morgan Chase technology blog
• JP Morgan technology teams
• Data Scientist Interview Questions
• JP Morgan Chase Interview Questions

FAQs about the JP Morgan Chase Data Scientist interview

How should I prepare for a JP Morgan Chase Data Scientist interview?

To prepare for a data scientist interview at JP Morgan Chase, brush up on your skills by practicing common data science problems and reviewing key concepts. Look through JP Morgan Chase’s technology blog, paying particular attention to ML and AI projects, as well as the way these tools are employed to benefit teams throughout the organization. Also practice your coding and data analytics skills, and general data science concepts.

How much do JP Morgan Chase Data Scientists make?

Here is the yearly compensation for JP Morgan Chase Data Scientists according to Levels:

• Analyst: $106,000
• Associate: $140,000
• Senior Associate: $161,000
• Vice President: $210,000
• Executive Director: $321,000

Does JP Morgan Chase offer data science internships?

JP Morgan Chase has an extensive internship and apprenticeship program offering roles in most major functions, including data science. The internships typically last nine weeks and give you the chance to apply your skills and see how they can be used in a financial analysis capacity. They post internships on their Jobs page.

How long is the JP Morgan Chase Data Scientist interview process?

Because there are many rounds and the interviews require coordination with multiple team members, the process can take anywhere between 6 to 12 weeks.

Do I need to have fintech or finance experience to be a data scientist at JP Morgan Chase?

When applying as a data scientist at JP Morgan Chase, the team members you interview with will want to see how you think through problems, and they’ll assess your grasp of the discipline of data science. While having institutional knowledge can help, you do not necessarily need to have worked for a financial or fintech organization to get an offer at JP Morgan Chase.