Airtable Data Scientist Interview Questions + Guide in 2024

Airtable is the no-code app platform that empowers people closest to the work to accelerate their most critical business processes. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable to transform how work gets done.

In this guide, we’ll tackle how they conduct their data science interviews, along with commonly asked Airtable data scientist interview questions to help you prepare better. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on an Airtable data scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Airtable 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 Airtable data scientist 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. However, be prepared as you may also be asked about your SQL skills and product case studies.

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 Airtable Data Scientist role is usually conducted through virtual means, including video conference and screen sharing. Questions in this one-hour interview stage may revolve around metrics, SQL concepts (no need to code), and product sense.

In the case of data scientist 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

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the Airtable office, varying with the role. Your technical prowess, including programming, experimentation, 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 Scientist role at Airtable.

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

1. What metrics/graphs/models would you use to analyze churn behavior for Netflix’s pricing plans?

Netflix has two pricing plans: $15/month or $100/year. An executive wants to analyze the churn behavior of users subscribing to either plan. What kinds of metrics, graphs, and models would you build to provide an overarching view of subscription performance?

2. How would you build a model to predict which merchants DoorDash should acquire in a new market?

As a data scientist at DoorDash, how would you build a model to predict which merchants the company should target for acquisition when entering a new market?

3. How would you value the benefit of keeping a hit TV show on Netflix?

Netflix executives are considering renewing a deal with another TV network for exclusive streaming rights to a hit TV series. The show has been on Netflix for a year. How would you approach valuing the benefit of keeping this show on Netflix?

4. How would you measure and address the success of LinkedIn’s newsfeed ranking algorithm?

1. How would you measure the success of LinkedIn’s newsfeed ranking algorithm?
2. If some success metrics for the newsfeed algorithm are increasing while others are decreasing, how would you approach this?

5. How would you determine if the results of an AB test for a landing page redesign are statistically significant?

We want to launch a redesign of a landing page to improve the click-through rate using an AB test. How would you infer if the results of the click-through rate were statistically significant or not?

6. Write a function calculate_rmse to calculate the root mean squared error of a regression model.

The function should take in two lists, one that represents the predictions y_pred and another with the target values y_true.

7. Write a query to get the last transaction for each day from a table of bank transactions.

Given a table of bank transactions with columns id, transaction_value, and created_at, write a query to get the last transaction for each day. The output should include the id of the transaction, datetime of the transaction, and the transaction amount. Order the transactions by datetime.

8. Write a function random_key that returns a key at random with a probability proportional to the weights.

Given a dictionary with weights, write a function random_key that returns a key at random with a probability proportional to the weights.

9. Write a function to get a sample from a standard normal distribution.

Create a function to generate a sample from a standard normal distribution.

10. Write an efficient function nearest_entries to find the closest element to N and its k-next and k-previous elements in a sorted list.

Given a sorted list of integers ints with no duplicates, write an efficient function nearest_entries that takes in integers N and k and finds the element closest to N, returning that element along with the k-next and k-previous elements of the list.

11. How would you explain what a p-value is to someone who is not technical?

Explain the concept of a p-value in simple terms to a non-technical person, focusing on its role in determining the significance of results in hypothesis testing.

12. How many more samples are needed to decrease the margin of error from 3 to 0.3?

Given a sample size (n) with a margin of error of 3, calculate the additional number of samples required to reduce the margin of error to 0.3.

13. How would you determine if the results of an AB test on click-through rate are statistically significant?

Describe the process of analyzing AB test results to determine if the observed differences in click-through rates are statistically significant.

14. How would you assign point values to letters in Spanish Scrabble if you don’t know Spanish?

If you need to build Scrabble for Spanish users and don’t know Spanish, how would you determine the point values for each letter?

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 Airtable data scientist interview include:

• Understand Airtable Products: Study Airtable’s range of products and use cases across different industries. Be ready to discuss how your skills can aid in driving Airtable’s business goals through data.
• Be Prepared for SQL Questions: Brush up on your SQL knowledge, specifically focusing on window functions, joins, and unions. Make sure you can explain when and why you would use these functions.
• Practice Analytical Storytelling: Airtable values the ability to translate raw data into actionable insights. Practice storytelling with data, ensuring you can communicate complex data insights clearly and effectively to a non-technical audience.

What is the average salary for a Data Scientist at Airtable?

According to Glassdoor, Data Scientists at Airtable earn between $149K to $226K per year, with an average of $183K per year.

What kind of skills and experiences are required for the Data Scientist role at Airtable?

Candidates should have 6-10 years of experience, preferably working with leadership teams at high-growth startups. Proficiency in SQL and tools like R or Python is required. Ability to build self-service data sets in Looker and excellent communication skills to translate complex data into actionable insights are also important.

What are the primary responsibilities of a Data Scientist at Airtable?

Data Scientists at Airtable drive in-depth financial and product analysis, develop executive dashboards, and provide analytical insights to the CEO and Leadership Team. They also support product development teams, manage the design and analysis of experiments, and build a strong data culture within the company.

What should I expect in terms of compensation for the Data Scientist role at Airtable?

Compensation varies based on location, skills, and experience. For work locations in the San Francisco Bay Area, New York City, and Los Angeles, the base salary ranges from $170,000 to $221,500 USD. For all other locations, including remote, the range is $153,000 to $199,300 USD. The package includes benefits, restricted stock units, and may include incentive compensation.

Considering a Data Scientist role at Airtable? As a candidate, you’ll be pivotal in shaping the future of how users interact with their innovative, no-code software creation platform. Despite some mixed experiences shared by applicants—ranging from comprehensive interviews to unexpected questions—this role promises high visibility and significant impact. If you seek to influence strategic decisions and drive insights across product and finance teams, Airtable offers a compelling opportunity.

For more insights about the company, check out our main Airtable Interview Guide. We’ve covered many potential interview questions, and crafted guides for other roles such as software engineer and data analyst, to help you navigate the process.

Good luck with your interview!