Affirm, Inc. - Data Analyst Interview Guide Overview

Affirm, Inc. is a financial technology company dedicated to reinventing banking by making it more transparent and customer-centric. Founded in 2012 by Max Levchin, Affirm provides innovative solutions for online point-of-sale financing, empowering consumers to break free from traditional credit pitfalls and better manage their financial wellness.

As a Data Analyst at Affirm, you will delve into robust data sets to extract actionable insights, ensure data accuracy, and help shape strategic decisions that impact millions of users. This role demands strong analytical skills, proficiency in SQL, and a deep understanding of statistical methods.

Thinking of joining Affirm? This guide will walk you through the interview process, commonly asked questions, and preparation tips to help you succeed. Welcome to your Affirm journey with Interview Query!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Affirm, Inc. as a Data Analyst. Whether you were contacted by an Affirm recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

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

The first call, typically lasting about 30 minutes, is with the recruiter who will ask generic HR questions, provide information about the company, work setting, and explain the interview process. In some cases, the Affirm hiring manager may also be present to answer your queries about the role and the company itself, and may indulge in surface-level technical and behavioral discussions.

Technical Phone Screen

Successfully navigating the recruiter round will present you with an invitation for the technical screening round, usually conducted through virtual means, including video conference and screen sharing. This stage often involves solving SQL problems and addressing questions related to data analysis.

In the initial technical phone screen, interviewers may provide a leetcode-style task with runtime constraints. Typical questions could range from SQL design challenges to data manipulation tasks. Interviewers expect you to communicate your thought process clearly, ask clarifying questions if needed, and solve the problems presented efficiently.

System Design Interview

Following the technical phone screen, you'll move on to a system design interview. This can involve complex scenarios where you need to design data models, ETL pipelines, or data visualization dashboards. Affirm may present you with a specific product use case or hypothetical scenarios where you need to showcase your problem-solving abilities and knowledge of best practices in data design.

Onsite Interview Rounds

The final stage is the onsite interview loop, which consists of a series of back-to-back interviews with various team members including product managers, engineers, and cross-functional partners. This can span several hours and may include:

• A challenge interview focusing on execution tasks such as identifying key performance indicators (KPIs) for a given product.
• Behavioral interviews designed to assess cultural fit and collaboration skills.
• Technical interviews focusing on specific projects you have worked on, including system design and SQL proficiency.

For instance, you might be asked to discuss a project you worked on, describe its technical stack, and answer questions about your approach to solving specific data issues.

Quick Tips For Affirm Data Analyst Interviews

• Prepare for Basics: Concentrate on foundational knowledge and basics, as technical screening may involve simpler problems but require clear and efficient solutions.
• Understand the Company's Products and Metrics: Familiarize yourself with Affirm’s core products and financial metrics. Knowing the business context can help you better respond to product-related questions.
• Be Ready for Behavioral Questions: Practice answering questions about your past projects, conflicts with stakeholders, and how you prioritize tasks. Be ready to discuss your day-to-day responsibilities and how they align with Affirm's work culture.

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

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests, and specify scenarios for their appropriate use.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify drawbacks in their current organization, suggest formatting changes, and describe common issues in "messy" datasets.

• What metrics would you use to evaluate the value of marketing channels? Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine the value of each marketing channel.

• How would you determine the next partner card for a company using customer spending data? With access to customer spending data, outline the process to identify the most suitable partner for a new credit card offering.

• How would you investigate the impact of a redesigned email campaign on conversion rates? Analyze the increase in new-user to customer conversion rates following a redesigned email journey, considering other potential influencing factors.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. 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.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• How does random forest generate the forest and why use it over logistic regression? Explain how random forest generates multiple decision trees and combines their results. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms and describe scenarios where bagging is preferred over boosting. Provide examples of tradeoffs, such as variance reduction in bagging and bias reduction in boosting.

• How would you evaluate and compare two credit risk models for personal loans?

• Identify the type of model developed by the co-worker for loan approval.
• Describe how to measure the difference between two credit risk models over a timeframe, considering monthly installments.

• List metrics to track the success of the new model, such as accuracy, precision, recall, and AUC-ROC.

• What’s the difference between Lasso and Ridge Regression? Explain the differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

• What are the key differences between classification models and regression models? Describe the main differences between classification and regression models, including their objectives, output types, and common use cases.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• What are the drawbacks of the given student test score data layouts, and how would you reformat them for better analysis? Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

• What is the expected churn rate in March for customers who bought a subscription since January 1st? You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

What is the average salary for a Data Analyst at Affirm, Inc.?

Q: What does the interview process for a Data Analyst position at Affirm look like? The interview process typically consists of three stages: a phone screen, a system design interview, and a final on-site interview. The process may include live coding challenges, behavioral questions, and product design problem-solving.

Q: What are some common questions asked during the interview? Common questions include "Why did you apply to this position?", "Why should we hire you?", and "Tell me about a project you're proud of." You may also encounter technical questions such as "If Metric X decreased by 5%, what would you do?" and case study challenges like designing a product feature.

Q: What is the company culture like at Affirm? Affirm places a strong emphasis on a positive and collaborative company culture. Interviewers often discuss the company's values and culture extensively during the interview process. The overall candidate experience is designed to be friendly, engaging, and well-organized.

Q: How can I prepare for the technical aspects of the interview? To prepare for the technical aspects of the interview at Affirm, focus on basics and runtime constraints. Platforms like Interview Query can be invaluable for practicing coding challenges and system design questions.

Q: What feedback can I expect throughout the interview process? Affirm is known for providing detailed feedback at various stages of the interview process. Recruiters and hiring managers often follow up promptly after each step to ensure that candidates are well-informed about the next stages and to address any doubts or questions.

Conclusion

Affirm, Inc. demonstrates a thorough and structured interview process tailored to gauge both technical skills and cultural fit. While experiences vary, many candidates appreciate the professionalism, clear communication, and detailed feedback throughout the stages. Preparation focusing on basic principles and familiarizing oneself with behavioral and product-specific questions seems to be key. Looking for more insights about Affirm, Inc.?

Check out our main Affirm Interview Guide, where we cover 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 Affirm'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 Affirm interview question and challenge.

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

Good luck with your interview!