Asana Data Scientist Interview Questions + Guide in 2024

Asana is a leading provider of work management software designed to help teams organize and track their work. Known for its collaborative and user-friendly platform, Asana has made significant strides in improving workplace productivity for companies of all sizes.

If you’re interested in the Data Scientist position at Asana, you’ll be joining a team dedicated to enhancing the company’s products and user experience through data-driven insights. The role involves a mix of technical and analytical skills, focusing on SQL, statistical analysis, machine learning, and product metrics. The interview process typically includes a series of technical screens, coding challenges, case studies, and behavioral interviews.

In this guide, Interview Query will walk you through the interview process for this position, providing valuable tips and commonly asked Asana data scientist interview questions. Let’s get started!

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

Recruiter/Hiring Manager Call Screening

If your application stands out, a recruiter from Asana will reach out for an initial phone screen. The recruiter will verify key details, such as your experiences and skill levels. Behavioral questions might also be included in this screening.

In some cases, the hiring manager may join the screening call to answer any of your questions regarding the role and company. They may also engage in surface-level technical and behavioral discussions. This call typically lasts about 30 minutes.

Take-Home Data Challenge

Candidates who pass the initial screening will receive a take-home data challenge. This task usually involves working with a dataset collected on Asana’s platform. You will need to build a predictive model or provide insights based on the data provided.

Be prepared to clean and wrangle the data to produce meaningful results. This stage is critical as it helps assess your problem-solving and analytical skills.

Technical Phone Interview

Following the take-home data challenge, candidates will typically have a technical phone interview with a data scientist or hiring manager from Asana. This interview often focuses on problem-solving, algorithms, and case studies.

Be prepared for questions related to statistical methods, machine learning, and SQL. Sometimes, they may ask you about your approach to the take-home challenge, so keep your project details handy.

Onsite Interview Rounds

Those who make it through the earlier stages will be invited for onsite interview rounds. The onsite loop usually consists of multiple interviews, including technical and behavioral assessments. Expect to discuss:

• Experimental design and A/B testing
• Statistical analysis and causal inference
• Machine learning models and their applications
• Product metrics and business strategy
• Relational data modeling and SQL

In some cases, you may be asked to present your take-home challenge results. These interviews are a chance for Asana to gauge your technical prowess and cultural fit within the company.

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

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

In an A/B test, how can you verify that the assignment to different buckets was random?

2. Why did the $10 reward group have a lower response rate than the control group?

You designed an experiment to measure the impact of financial rewards on users’ response rates. The treatment group with $10 rewards had a 30% response rate, while the control group without rewards had a 50% response rate. Explain what happened and how to improve the experimental design.

3. How would you address conflicting success metrics for LinkedIn’s newsfeed algorithm?

If some success metrics for LinkedIn’s newsfeed algorithm are improving while others are declining, how would you approach this situation?

4. How would you measure the impact of entering the podcast space on customer lifetime value?

A media company that earns from monthly subscriptions is considering entering the podcast space. How would you measure the impact of this move on customer lifetime value?

5. How would you explain the results and improve the experimental design measuring financial rewards’ impact on response rates?

You designed an experiment where the treatment group with $10 rewards had a 30% response rate, while the control group without rewards had a 50% response rate. Explain the results and suggest improvements for the experimental design.

6. What is the probability that Bob is negative for the disease given the test’s false positive and false negative rates?

Bob tested positive for a disease, while six close friends tested negative. The test has a 1% false positive rate and a 15% false negative rate. Calculate the probability that Bob is negative for the disease.

7. What are the assumptions of linear regression?

List and explain the key assumptions that must be met for linear regression analysis to be valid.

8. How would you evaluate a model that predicts news relevance on X?

Given a model that predicts whether a piece of news is relevant when shared on X, describe the methods and metrics you would use to evaluate its performance.

9. How would you optimize the ratio of public versus private content in a feed?

Given different types of posts (e.g., baby pictures, Tasty videos, birthday posts), explain how you would build a model to rank them. Specify the features you would use and the metrics you would track to optimize the ratio of public versus private content.

10. Create a function find_bigrams to return a list of all bigrams in a sentence.

Write a function called find_bigrams that takes a sentence or paragraph of strings and returns a list of all its bigrams in order. A bigram is a pair of consecutive words.

11. 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, datetime, and transaction amount, ordered by datetime.

12. Create a function find_change to find the minimum number of coins for a given amount.

Write a function find_change to find the minimum number of coins that make up the given amount of change cents. Assume we only have coins of value 1, 5, 10, and 25 cents.

13. Design a function to simulate drawing balls from a jar.

Write a function to simulate drawing balls from a jar. The colors of the balls are stored in a list named jar, with corresponding counts of the balls stored in the same index in a list called n_balls.

14. Create a function calculate_rmse to compute the root mean squared error.

Write a function calculate_rmse to calculate the root mean squared error of a regression model. The function should take in two lists, one representing the predictions y_pred and another with the target values y_true.

To help you succeed in your Asana data scientist interviews, consider these tips based on interview experiences:

• Emphasize A/B Testing Knowledge: Asana places a strong focus on experimentation and measuring user behavior. Brush up on A/B testing and causal inference methods.
• Prepare for Take-Home Challenges: Take-home assignments are a significant part of the interview process. Make sure you can efficiently clean, analyze, and model data.
• Show Your Product Sense: Understand Asana’s product and market. Be ready to provide insights on product metrics and discuss how data can drive product decisions.

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

What skills are most important for a Data Scientist at Asana?

Asana values strong technical skills in SQL and other modern programming language like Python or R. Experience with A/B testing, machine learning, and statistical methods is crucial. Additionally, being able to communicate complex ideas to diverse audiences and working effectively with cross-functional teams is essential.

What is the culture like at Asana?

Asana boasts a collaborative and inclusive culture, focused on innovation and teamwork. Employees are encouraged to take risks, learn from mistakes, and continuously improve. The company is committed to diversity and providing equal opportunities for all.

Curious about Asana’s interview process for data science roles? Look no further! Dive deep into our comprehensive Asana Interview Guide where we cover various interview questions you might encounter. Explore our guides for roles like software engineer and data analyst to understand Asana’s interview approach across different positions.

At Interview Query, we equip you with the knowledge, confidence, and strategic insights to excel in every interview challenge. Discover all our company interview guides and get ready to ace your Asana interview. Good luck!