Chime Data Analyst Interview Questions + Guide in 2024

Chime is a dynamic financial technology company committed to making financial progress accessible to all. With an innovative approach to banking and financial services, Chime focuses on providing transparent, fair, and helpful solutions to empower its members.

As a Data Analyst at Chime, you will be integral to developing data-driven products and insights that elevate the banking experience for millions. This role involves performing sophisticated data analysis, crafting dashboards, and collaborating with cross-functional teams to inform product development and strategic decision-making.

In this Interview Query guide, we will take you through the interview process, commonly asked Chime data analyst interview questions, and provide essential tips to help you succeed. Let’s get you prepared!

The interview process usually depends on the role and seniority; however, you can expect the following on a Chime data analyst interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Chime Talent Acquisition Team will contact you and verify key details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Chime data analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Chime data analyst role is usually conducted virtually, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Chime’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. In addition, your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Case studies and similar real-scenario problems may also be assigned depending on the position’s seniority.

Onsite Interview Rounds

After a second recruiter call outlining the next stage, you’ll be invited to attend the on-site interview loop. During your day at the Chime office, multiple interview rounds, varying with the role, will be conducted. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data analyst role at Chime.

Typically, interviews at Chime vary by role and team, but commonly, Data Analyst interviews follow a fairly standardized process across these question topics.

1. How would you set up an A/B test to optimize button color and position for higher click-through rates?

A team wants to A/B test multiple changes in a sign-up funnel. For instance, they want to see if changing a button from red to blue and/or from the top to the bottom of the page will increase click-through rates. How would you set up this test?

2. Why are job applications decreasing despite a steady number of job postings?

You are analyzing the metrics of a job board and notice that while the number of job postings per day has remained constant, the number of applicants has been steadily decreasing. Why might this be happening?

3. Can unbalanced sample sizes in an A/B test result in a bias towards the smaller group?

You need to analyze the results of an A/B test, with one variant having a sample size of 50K users and the other having 200K users. Can the unbalanced sizes lead to bias towards the smaller group?

4. How can you check if the assignment to A/B test buckets was truly random?

In an A/B test, how would you verify that the assignment to various buckets was truly random?

5. How would you assess the validity of an A/B test result with a 0.04 p-value?

Your company is running a standard control and variant A/B test on a feature to increase conversion rates on the landing page. The PM finds a p-value of 0.04 in the results. How would you assess the validity of this result?

6. What are time series models, and why are they needed over simpler regression models?

Explain what time series models are and discuss why they are necessary when simpler regression models might not suffice.

7. What happens when you run logistic regression on perfectly linearly separable data?

Given a perfectly linearly separable dataset, describe the outcome of running logistic regression on it.

8. What is the probability of rolling at least one 3 with 2 dice?

You are playing a dice game with 2 dice. Calculate the probability of rolling at least one 3. Extend this to (N) dice.

9. Can an AB test with unbalanced sample sizes result in a bias towards the smaller group?

Analyze the potential bias in an AB test where one variant has 50K users, and the other has 200K users due to the unbalanced sample sizes.

10. What happens to the target metric after applying a new UI that won by 5% in an AB test?

If a new UI tested on a random subset of users wins by 5% on the target metric, predict the change in the metric after applying the new UI to all users, assuming no novelty effect.

11. What are the key differences between classification models and regression models?

Explain the primary distinctions between classification and regression models, focusing on their objectives, output types, and typical use cases.

12. When would you use a bagging algorithm versus a boosting algorithm?

Compare the use cases for bagging and boosting algorithms, providing examples of the tradeoffs between the two.

13. What’s the difference between Lasso and Ridge Regression?

Explain the differences between Lasso and Ridge Regression, focusing on their regularization techniques and effects on model coefficients.

14. How does random forest generate the forest and why use it over logistic regression?

Describe how a random forest generates its ensemble of trees and discuss the advantages of using random forest over logistic regression.

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Chime interview include:

1. Know Your Chime Products: Chime questions are standardized and rely heavily on situational scenarios with their products. Study Chime’s large breadth of products and understand how you would personally improve or test them.

2. Be Data Driven: Chime’s data science interviews assess how well you can use data science to provide business-driving insights. Brush up on your knowledge of statistics and probability, given that these questions can be some of the hardest to solve.

3. Embody the Spirit: Chime’s core culture is collaborative, employee-focused, and values innovation. Practice responding to behavioral questions with answers that touch on Chime’s core values.

What is the average salary for a Data Analyst at Chime?

According to Glassdoor, data analysts at Chime earn between $132K to $183K per year, with an average of $155K per year.

What are some of the responsibilities of a Data Analyst at Chime?

As a Data Analyst at Chime, you will develop, test, launch, and scale member banking experience products. You’ll be involved in experimentation, user behavioral analysis, statistical and data science modeling, and dashboard development. You’ll work closely with various teams like product managers, engineers, and marketing to foster a data-driven culture and support decision-making processes.

What skills and qualifications are required to be a successful Data Analyst at Chime?

Candidates should have 4+ years of experience in data-focused roles, particularly in B2C product analytics and FinTech. Proficiency in SQL, R, or Python, as well as BI/Visualization tools such as Looker, Tableau, or PowerBI, is essential. Experience in leading experimentation and statistical analysis and excellent stakeholder management skills are also key.

What is the company culture like at Chime?

Chime has a value-driven culture that prioritizes empathy, innovation, and a passion for supporting members’ financial progress. The company promotes a diverse and inclusive environment where employees of various backgrounds and ideas collaborate to make a meaningful difference.

If you want more insights about the company, check out our main Chime Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Chime’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Chime data analyst interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!