Intuit Data Scientist Interview Guide

Intuit Inc. is one of the biggest small business and financial technology companies in the world. The company develops and sells business and financial management software solutions (QuickBooks), tax solutions for individuals (TurboTax), and personal finance solutions (Mint and Credit Karma now). Founded in 1983, Intuit has since emerged as a leading fin-tech company with over 50 million customers served worldwide in over nine countries.

Intuit generates tons of customer data yearly, connecting all of its products together. As a data-driven company, data science is at the core of everything, and Intuit has over the years been leveraging data science in advanced analytics and machine learning tools to improve their customers’ financial lives.

Data scientist roles at Intuit vary across different teams, and the specific roles of a data scientist within each team will be heavily determined by the needs of that group. From teams such as Small businesses to Machine Learning Futures, data scientist teams at Intuit analyze data and deploy ML and AI models to solve business-related problems. Generally speaking, the scope of data science at Intuits spans from business analytics and data engineering, and the tools used may range from basic analytics to machine learning and deep learning.

Required Skills

Intuit’s preferred data science hiring requirements may vary across specific teams and groups, but in general, hire only talented and qualified applicants with a minimum of 3 years (5+ years for senior-level) in data science roles.

Other basic requirements for hiring include:

• BS, MS, or PhD in Statistics, Applied Math, Operations Research, Computer Science, Physics, Engineering, or related fields, or equivalent experience.
• Knowledgeable with data science tools and frameworks (i.e., Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
• 1 to 3+ (5+ years for senior-level) years’ experience with a general-purpose programming language (e.g., Python, C, Java, etc.).
• Domain expertise in subjects such as experimental design and multivariate ab testing.
• Strong interpersonal and communication skills in order to effectively contribute to technical teams and make presentations to a variety of technical and business personnel.

What kind of data science role?

Data science roles at Intuits are spread across a wide range of groups. On the surface, a data scientist at Intuit is someone who uses advanced analytics tools, machine learning, NLP, and AI algorithms to provide business-impact recommendations. However, specific roles may span from product-specific analytics teams embedded on a team to machine learning engineering implementation. Depending on the group assigned, the functions of a data scientist or machine learning engineer at Intuit may include:

• Risk Research & Intelligence: Building and prototyping algorithms and applications to improve security and anti-fraud on top of the collective financial data of 60 million consumers and small businesses
• Smart Money Services: Leveraging data mining and machine learning techniques to manage credit and fraud risk in payments and payroll.
• Small Business Data Science team: Using industry-leading analytics tools and techniques to drive user growth and retention in small businesses.
• Core Data Science Team: Develop, design, and integrate ML models into production. Collaborate and build AI solutions for all internal teams e.g. Engineering, HR, Finance & Legal, etc.
• Customer Success Data Team: Pull out insights from customer success data and apply them to all intuits products (TurboTax, QuickBooks, Mint, etc.).

Intuit’s data science interviews start with an initial phone call from a recruiter, followed by a technical video interview of past relevant projects and a take-home challenge. After finishing through the initial stages, an onsite interview will be scheduled, which consists of four 45-minute long interviews with various team members, technical manager, and the product manager.

Initial Screen

The initial interview is a resume-based phone interview with an HR or recruiter. This interview aims to assess your skills and past projects to see if you are a great fit for the team you are applying for. Questions in this screening are standard resume-based questions.

Technical Screen

The technical screen at Intuit is after the recruiter screen. It is done either with Karat, an external interviewing service, or with an Intuit hiring manager. Interview questions for data science roles consists of testing analytics and coding skills in SQL and Python, respectively.

Here’s a sample question that you can try:

Let’s say you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

The interview is an hour-long, and it’s pertinent to display a clear aptitude for technical ability. The interviewer will also go over any past projects to get a sense of your past experience. Really nail down your resume and how to talk about your projects in-depth and how they relate to applied machine learning.

The Take-Home Challenge

Intuit gives a data challenge before the onsite interview, and applicants are required to complete this within four hours of receiving the take-home. The take-home challenge comprises a standard Intuit case study dataset on TurboTax. You’ll have to run analytics in SQL and work on a machine learning problem on the dataset.

The Onsite Interview

The onsite interview at Intuit comprises four interview rounds (two technical, one data-challenge presentation, and one behavioral). Technical questions in this interview are mainly open-ended and span across basic statistical concepts (A/B Testing), modeling, experimental design, SQL, and machine learning algorithms. In general, the onsite interview at Intuit looks like this:

• Data challenge presentation: In this round, candidates are expected to create dashboards of the data given and coherently explain to the interviewer the type of analysis done and how well the data was explored. This will require coding in a live environment and analyzing the data while talking through what you’re doing.
• Technical interview with a technical manager talking about experience and machine learning concepts.
• Technical interview with a data scientist that involves coding SQL and algorithms plus probability and statistics questions.
• Behavioral interview with product managers and executives. Unlike interviews in other big tech companies, the behavioral interview at Intuit will focus on their culture and values and how well candidates can relate to non-technical staff, including higher-ups.

• Data science interview questions at Intuit cover a wide range of analytical concepts, statistical modeling, experimental designs, and machine learning algorithms. Prepare beforehand on how you can apply these data science concepts to Intuit-related business problems.

• Practice coding on a whiteboard and analyzing data in an existing environment.

• Intuit has a great work culture. Reading up about their culture and core values will aid you in the behavioral interview.

• How does boosting work?
• Let’s say you can play a coin-flipping guessing game either once or a 2 out of 3 games. What is the best strategy for winning?
• Given a long array that you can’t store, how do you find the median?
• What are the limitations of linear regression?
• Describe how a random forest works under the hood.
• Implement an iterator function that takes three iterators as the input and sorts them.
• What features would you add to a model that doesn’t already exist?
• If there was a feature that 100% of the users used, would it be a good feature?
• What’s an adequate rebalance of an imbalanced dataset?

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