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.

Joining Airtable as a Data Scientist is a unique opportunity to shape the company's future by providing critical financial insights directly to executive leadership. This role involves robust SQL, product sense, and experimentation skills, and requires effective communication to translate data into strategic decisions. You'll have a direct impact on Airtable's growth, making this an exciting time to join the team.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Airtable as a Data Scientist. Whether you were contacted by an Airtable 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 Airtable 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.

In some cases, 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 present you with an invitation for the technical screening round. Technical screening for the Airtable Data Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long 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. Apart from these, your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

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, varying with the role, will be conducted during your day at the Airtable office. 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.

Quick Tips For Airtable Data Scientist Interviews

• 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.

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

• 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?

• 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?

• 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?

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

• How would you measure the success of LinkedIn’s newsfeed ranking algorithm?

• If some success metrics for the newsfeed algorithm are increasing while others are decreasing, how would you approach this?

• 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?

• 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.

• 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.

• 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.

• Write a function to get a sample from a standard normal distribution. Create a function to generate a sample from a standard normal distribution.

• 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.

• 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.

• 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.

• 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.

• 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 develop a model to identify which merchants the company should target for acquisition when entering a new market?

• 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?

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

Q: What is the interview process like for a Data Scientist position at Airtable?

The interview process typically begins with an initial interview covering metrics, product sense, and SQL (without coding). The onsite interviews include multiple rounds, each lasting about an hour, focusing on SQL, product sense, experimentation, and a one-hour presentation of a take-home assignment.

Q: 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.

Q: 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.

Q: 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.

Q: How can I best prepare for the technical interviews at Airtable?

To prepare, practice SQL, statistics, and problem-solving through Interview Query. Focus on understanding syntax and its usage to handle unexpected questions effectively.

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.

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 ace your Airtable Data Scientist interview.

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!