Datadog Data Scientist Interview Questions + Guide in 2024

Datadog is a leading global SaaS company known for its innovation in infrastructure monitoring and cloud migration solutions. Datadog’s mission is to simplify the complexity of the cloud age for organizations of all sizes, helping them achieve digital transformation seamlessly.

Joining Datadog as a Data Scientist means being a part of their Data Science organization, where you will contribute to designing, implementing, and scaling new and existing features. This role spans across the entire data science lifecycle, from design to production.

If you’re excited about contributing to high-impact projects and pioneering in data science technologies, this guide is for you. We’ll walk you through the interview process, commonly asked Datadog data science interview questions, and valuable tips to ace your interview. Let’s get started!

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Datadog 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.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Datadog Data Scientist role is usually conducted virtually, including through video conferences and screen sharing. Questions in this 1-hour interview stage may revolve around statistics, machine learning concepts such as linear regression hypotheses, algorithmic complexity, and Leetcode easy/medium coding problems.

Depending on the seniority of the position, you will be asked to present a technical project you have worked on. It’s crucial to bring technical depth and demonstrate your problem-solving skills effectively.

Take-Home Assignments

If you pass the technical virtual interview, you will be given a take-home assignment to complete within a designated timeline. Typically, it will contain two parts: a data analysis problem and a coding problem (such as time series analysis), requiring you to write clean and well-documented code. Ensure you understand what points will be evaluated and address them thoroughly.

Onsite Interview Rounds

Once your take-home assignment is reviewed and found satisfactory, you will be invited for a series of onsite interview rounds. These may include:

1. Interview with a Software Engineer: Focused on live coding exercises, past projects, and software engineering principles.
2. Interview with a Lead Data Scientist: Involves a debrief of your take-home data analysis and a deeper dive into data science problem-solving (e.g., discussing a system for anomaly detection in time series).
3. Interview with the Hiring Manager: This might cover both behavioral and technical aspects, where you need to prepare questions for your interviewers and discuss any open-ended problems.

Typically, interviews at Datadog vary by role and team, but commonly, data scientist interviews follow a fairly standardized process across these question topics.

1. Create a function n_frequent_words to find the top N frequent words in a paragraph.

Given a paragraph string and an integer N, write a function n_frequent_words that returns the top N frequent words in the posting along with their frequencies.

2. Given an example paragraph string and an integer N, write a function n_frequent_words that returns the top N frequent words in the posting and the frequencies for each word.

Example:

Input:

posting = """
Herbal sauna uses the healing properties of herbs in combination with distilled water.   
The water evaporates and distributes the effect of the herbs throughout the room.   
A visit to the herbal sauna can cause real miracles, especially for colds. 
"""  
n = 3

Output:

n_frequent_words(posting,N) = [
    ('the', 6), 
    ('herbal', 2), 
    ('sauna', 2),
]

3. What are the Z and t-tests, and when should you use each?

Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

4. How would you reformat student test score data for better analysis?

Given two datasets of student test scores, identify drawbacks in their current format. Suggest formatting changes and discuss common issues in “messy” datasets.

5. 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.

6. How would you determine the next partner card using customer spending data?

With access to customer spending data, outline the process to identify the best partner for a new credit card offering.

7. How would you investigate the impact of a redesigned email campaign on conversion rates?

Analyze whether an increase in new-user-to-customer conversion rates is due to a redesigned email campaign or other factors, considering historical data and potential external influences.

8. How does random forest generate the forest and why use it over logistic regression?

Explain how random forest generates multiple decision trees and why it might be preferred over logistic regression in certain scenarios.

9. When would you use a bagging algorithm versus a boosting algorithm?

Compare two machine learning algorithms and provide examples of tradeoffs between using bagging and boosting algorithms.

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

1. Identify the type of model developed by a co-worker for loan 
   approval.
2. Explain how to measure the difference between two credit risk 
   models over time.
3. List metrics to track the success of the new model.

11. What’s the difference between Lasso and Ridge Regression?

Describe the key differences between Lasso and Ridge Regression techniques.

12. What are the key differences between classification models and regression models?

Explain the main differences between classification models and regression models.

13. 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?

14. What are the drawbacks of the given student test score datasets, and how would you reformat them?

Assume you have data on student test scores in two layouts. Identify the drawbacks of these formats, suggest formatting changes for better analysis, and describe common problems in “messy” datasets.

15. 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, calculate the expected churn rate in March for all customers who bought the product since January 1st.

16. How would you explain a p-value to a non-technical person?

Describe what a p-value is in simple terms for someone without a technical background.

Here are some tips to help you prepare for your upcoming Datadog data scientist interview:

• Show Technical Depth in Your Projects: When presenting your projects, emphasize the technical challenges and how you resolved them. This will enable the interviewer to gauge your expertise.

• Effective Communication: For live coding problems, articulate your reasoning and plan before diving into the code. Clear communication helps the interviewers follow your thought process. For additional practice, consider using our AI interviewer to receive real-time feedback on how you explain your code.

• Prepare for Open-Ended Questions: These questions assess your problem-solving skills and creativity. Show your reasoning, consider edge cases, and discuss potential challenges and solutions.

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

What is the culture and work environment like at Datadog?

Datadog promotes a collaborative, people-first environment where creativity and innovation are highly valued. The company operates as a hybrid workplace, encouraging a balance between office interaction and remote work. It’s a space where taking smart risks and solving tough problems are celebrated, fostering continuous personal and professional growth.

What benefits and growth opportunities does Datadog offer?

Datadog offers competitive salaries with stock equity options, continuous professional development, mentorship programs, and a supportive, inclusive company culture. Employees also have access to comprehensive healthcare benefits, fitness reimbursements, and various internal community guilds. The company encourages attendance and participation in industry conferences and meetups.

As a rapidly evolving market leader in SaaS solutions, Datadog continues to seek dynamic and innovative Data Scientists to join their esteemed team.

Focusing on the structured interview process and honing your technical expertise will equip you to make a lasting impression.

Good luck with your interview journey!