Atlassian Data Scientist Interview Questions + Guide in 2024

Atlassian Data Scientist Interview Questions + Guide in 2024Atlassian Data Scientist Interview Questions + Guide in 2024


Atlassian, a leading Australian-American enterprise software firm, specializes in solutions that enhance teamwork, project management, and the software development lifecycle.

As an Atlassian data scientist, you will play a pivotal role in steering the company’s strategic initiatives, diving deep into projects that leverage big data, machine learning, and predictive analytics. Working closely with software engineering teams, you’ll develop algorithms and models that underpin Atlassian’s leading products.

This comprehensive guide will explore questions frequently asked during Atlassian interviews and provide strategies for approaching them confidently.

What Is the Interview Process Like for a Data Scientist Role at Atlassian?

Atlassian’s data science interviews generally focus on SQL, product sense, and questions on machine learning and Python.

Step 1: Recruiter Screening

This stage usually has two rounds. First, the recruiter will get in touch with you to ask general questions and provide an overview of the interview process. Then, a hiring manager will contact you to understand your background and talk about the team/role you have applied for.

Prepare answers to commonly asked behavioral questions to ace this round. Later in this article, we’ll share a list of the top behavioral questions.

Step 2: Technical Interviews

You will next be invited to a series of interviews over video calls, where you’ll be asked SQL, Python, statistics, and ML questions. Prepare for this round by brushing up on important statistical concepts and practicing commonly asked SQL problems.

Step 3: Virtual On-Site Rounds

Next, you’ll be invited to a mix of technical, behavioral, and case study rounds. Here’s what they may look like:

  1. Values Interview: A senior Atlassian employee from a different team will evaluate your background and cultural fit within the company. To prepare, read Atlassian’s core values and prepare some responses on how your values align with theirs.
  2. Business Case Interview: This round tests your critical thinking skills and ability to structure a problem, ask clarifying questions, and brainstorm collaboratively. Ask relevant questions and think aloud when solving the case study.
  3. Advanced SQL Round: This interview evaluates your application of aggregate and window functions.
  4. Stakeholder Round: This interview will be a mix of business and behavioral questions with potential colleagues.

What Questions Are Asked in an Atlassian Data Scientist Interview?

1. Describe a challenging data science project you handled.

You’ll face a lot of complex decision-making at Atlassian, so you need to demonstrate your experience handling such situations.

How to Answer

Focus on a project you feel comfortable discussing in depth. Detail your approach, strategies, and impact. Be authentic and demonstrate that you worked collaboratively with your team and stakeholders.


“I led a project to optimize investment strategies in my previous firm. The challenge was integrating disparate data sources while ensuring model accuracy. My approach involved collaborating with cross-functional teams to refine data integration and iteratively improving the model based on stakeholder feedback. The outcome was a 15% improvement in prediction accuracy, significantly aiding our decision-making.”

2. Why do you want to join Atlassian?

Interviewers will want to know why you chose the data scientist role at Atlassian. They want to establish whether you’re passionate about the company’s culture and values or your interest is more opportunistic.

How to Answer

Demonstrate knowledge of Atlassian’s work, culture, and the opportunities that attract you to the company. Be honest and specific about how Atlassian’s offerings align with your career goals.


“Working at Atlassian would give me a chance to be part of a team that values innovation and promotes learning. I’m intrigued by Atlassian’s creative approach to solving collaboration challenges and the opportunity to work with diverse teams.”

3. Tell us about a time when you explained complex data science concepts to non-technical stakeholders. How did you ensure they understood?

Your ability to communicate complex ideas effectively is non-negotiable since you will be expected to participate in cross-functional teams and projects.

How to Answer

Highlight your communication skills through a specific instance from a past project. Use the STAR storytelling method: Discuss the Situation you were challenged with, the Task you decided on, the Action you took, and the Result of your efforts.


“In a past project, I was tasked with explaining the outcomes of a predictive model to our marketing team. I used analogies related to their daily work to illustrate how the model functions and its relevance to their campaigns, avoiding unnecessary technical jargon. I followed up with a Q&A session to address any doubts. This extra effort went a long way in promoting team dynamics and ensuring that the marketing team felt included in the technical conversations.”

4. How do you prioritize multiple deadlines?

In a global organization like Atlassian, you will work across teams, projects, and geographies. Time management and organization are essential skills for success.

How to Answer

Emphasize your ability to differentiate between urgent and important tasks. Mention any tools or frameworks you use for time management and showcase your ability to adjust priorities.


“In a previous role, I often juggled multiple projects with tight deadlines. I prioritized tasks based on their impact and deadlines using a combination of the Eisenhower Matrix and Agile methodologies. I regularly reassessed priorities to accommodate changes and communicated proactively with stakeholders about progress and any potential delays.”

5. Summarize your previous work experience.

This will help your interviewer understand which team or role you’d be well suited for.

How to Answer

Highlight your contributions, the technologies you’ve worked with, and any achievements or learnings that would benefit Atlassian’s work environment. Keep your response concise and relevant to the expected position.


“Throughout my 4-year career in data science, I’ve been fortunate to contribute to a wide array of projects across different sectors, including tech startups, healthcare, and e-commerce. At my first job with a tech startup, I focused on building predictive models to enhance customer insights and personalization features. This work involved extensive use of Python and R for statistical analysis and machine learning, as well as SQL for database management.

Transitioning into the healthcare sector, I led a team developing data analytics platforms that used machine learning to predict patient outcomes and optimize treatment plans.

Most recently, I worked on a project in an e-commerce company to implement a recommendation system that evaluated user behavior to personalize shopping experiences, boosting sales and customer retention.

With a deep understanding of data science technologies and a proven track record of leading successful projects, I am eager to bring my expertise to Atlassian.”

6. Given a list of tuples featuring names and grades on a test, write a function to normalize the values of the grades to a linear scale between 0 and 1.

You will need to demonstrate basic data manipulation problem skills in Python, as such operations are necessary for a data scientist’s daily coding responsibilities.

How to Answer

Outline your approach, which should involve finding the minimum and maximum grades and applying a formula to normalize each grade.


“My approach would be to extract the grades from the list of tuples, find the minimum and maximum grades, and then normalize each grade using the formula: (grade - min_grade) / (max_grade - min_grade).”

7. Given a table of user actions with columns user_id, action, and timestamp, can you write an SQL query to find the daily active users over the past month?

Understanding user engagement through daily active users (DAUs) is crucial for monitoring the health and growth of Atlassian’s products.

How to Answer

The strategy here involves writing an SQL query that counts unique user_ids for each day within the last month.


“I would write a query that filters the records to only include actions within the last 30 days from the current date. I would group these results by the date, extracting the date from the timestamp to ensure the grouping is done daily, not including time. For each group, I’d count the distinct user_id s to find the DAU.”

8. Given a table called user_experiences, write a query to determine the percentage of users that held the title of “data analyst” immediately before holding the title “data scientist.

This tests your skills in handling sequential data, a common scenario when analyzing user behavior.

How to Answer

The key here is to create a logical sequence of titles for each user and then calculate the percentage of users whose data analyst position immediately preceded the data scientist position.


“I’d filter the user_experiences table to only include records with titles of ‘data analyst’ or ‘data scientist.’ Then, I’d use a window function to assign a rank or row number to each user’s experiences, ordered by the date or sequence of their roles. I’d look for patterns where the ‘Data Scientist’ role directly follows the ‘Data Analyst’ role for a user. This can be done by comparing the sequence numbers or dates of these roles for each user.”

9. How would you measure the success of a new feature in Jira?

This question evaluates your ability to apply data science principles to product management.

How to Answer

Discuss a multifaceted approach that includes qualitative and quantitative metrics. Mention specific KPIs, user satisfaction surveys, and impact on overall product usage. Also, consider A/B testing to measure the performance of the new feature against a control group.


“I would start by defining clear objectives for success, such as increased user engagement, improved task completion times, or higher user satisfaction. Quantitatively, I’d analyze metrics like the adoption rate of the feature among the target user base, changes in user activity levels, and any observable impact on key performance indicators like daily active users or retention rates. For more direct feedback, conducting user satisfaction surveys and collecting qualitative feedback through user interviews or feedback forms can provide insights into the feature’s usability and value to users. Additionally, implementing A/B testing could offer concrete data as well.”

10. Given a table of product subscriptions with a subscription start date and end date for each user, write a query that returns true or false whether or not each user has a subscription date range that overlaps with any other completed subscription.

Understanding overlapping subscriptions can help Atlassian optimize its subscription models or identify potential areas where users see value in maintaining or upgrading their subscriptions.

How to Answer

Suggest a self-join on the subscription table to compare every subscription with every other subscription for the same user. Remember to account for any scenario where a subscription is compared to itself.


“I’d perform a self-join on the subscription table while ensuring not to compare a subscription with itself. Then, I would examine the start date of one subscription against the end date of the other subscription for each pair. An overlap occurs if the start date of one subscription is earlier than the end date of another, and simultaneously, its end date is later than the start date of the other subscription. For each user, the query would then use a case statement to return ‘true’ if any such overlap is detected among their subscriptions, and ‘false’ if no overlaps are found.”

11. What data would you look at to understand churn rates in Trello, and what steps might you take to address them?

Analyzing churn rates for Atlassian’s products is essential for maintaining and growing the user base.

How to Answer

Highlight the importance of a comprehensive analysis involving both quantitative and qualitative data.


“I would look at quantitative data such as user activity logs. Additionally, examining subscription data for changes in plan types or cancellation trends could offer insights into financial or value perception issues. Qualitative data from exit surveys and customer support tickets would provide context to the numbers.

Steps to address churn could include improving user onboarding to ensure new users fully understand Trello’s value, creating targeted engagement campaigns for users showing early signs of decreased activity, and addressing specific pain points identified through user feedback.”

12. Looking at our weekly metrics, you see a slow decrease in the average number of comments per user from January to March. Why might the average number of comments per user be decreasing, and what metrics would you look into?

For a company like Atlassian, gaining a deeper understanding of engagement metrics is crucial for preemptive action to reverse negative trends and improve product health. So, your critical thinking skills as a data scientist are vital.

How to Answer

Ask clarifying questions, state your assumptions clearly, and cite ways to validate your hypotheses.


“A decrease in the average number of comments could indicate several issues. First, it might suggest changes in the user base, such as an influx of new users who are less active or existing users becoming less engaged over time. To understand this, I would look into metrics like the new user acquisition rate versus user churn and engagement metrics for different user cohorts over time.

Another reason could be changes to the product or its environment, such as a recent update that made commenting less intuitive or necessary. Investigating the adoption and usage rates of recent features, along with user feedback from surveys or support tickets during the same period, could offer insights into product-related causes.

External factors, such as seasonal changes or shifts in work patterns due to holidays or global events, could also impact user engagement. Comparing the trend with the same period in previous years and examining broader industry or global trends might help identify these external influences.

Lastly, it’s crucial to consider the overall user experience and satisfaction, which could be affected by issues like increased bugs or performance problems. Metrics like load time, error rates, and support ticket volumes related to commenting features would be valuable to examine in this context.”

13. What are the benefits of feature scaling in a logistic regression model?

This is asked to assess your understanding of data preprocessing and its impact on model accuracy and performance, crucial for data-driven financial decision-making.

How to Answer

Focus on how feature scaling aids in faster convergence during training, ensures uniformity in feature influence, and enhances the interpretability of model coefficients. Talk about the practical implications of these benefits.


“Feature scaling standardizes the range of independent variables, leading to faster convergence during optimization. In the context of Atlassian, where logistic regression might be used to predict user behaviors or classify text in support tickets, feature scaling ensures that all aspects of the data are given equal consideration in the predictive model, leading to more accurate and reliable insights.”

14. Let’s say your manager has tasked you with computing the average salary of a data scientist using a recency-weighted average. Write the function to compute the average salary of a data scientist given a mapped linear recency weighting on the data.

Recency-weighted averages are an essential statistical method to analyze trends where market rates fluctuate significantly.

How to Answer

Explain the concept and its relevance in data analysis. In your function, outline how you would assign greater weight to more recent salaries.


“I would write a function that takes a list of salaries from the past ‘n’ years. The function will assign a linearly increasing weight to each year’s salary, with the most recent year having the highest weight. This approach ensures that recent trends in data scientist salaries have a more significant impact on the computed average, reflecting the current market conditions more accurately.”

15. Given a dataset containing millions of Bitbucket repository actions, describe how you would preprocess the data for a machine learning model predicting a user’s next action.

This question assesses your ability to handle and preprocess large datasets.

How to Answer

Describe steps involving data cleaning, feature selection, engineering, and scaling. Emphasize the importance of understanding the context of user actions within Bitbucket to create meaningful features.


“After cleaning the data, I’d focus on feature selection and engineering. Dimensionality reduction could be applied to manage the dataset’s scale and improve model efficiency. Feature scaling would ensure all variables contribute proportionately to the predictive model. Lastly, I would encode categorical variables like action types to make them suitable for the machine learning algorithm.”

16. Given a table of bank transactions with columns: id, transaction_value, and created_at representing the date and time for each transaction, write a query to get the last transaction for each day.

As a data scientist at Atlassian, you’ll need to handle time-series data to analyze activity patterns or financial transactions.

How to Answer

Demonstrate your knowledge of SQL aggregate and window functions to partition the data.


“I’d convert the created_at timestamp to a date format to group transactions by their respective days. Then, employing the ROW_NUMBER() window function, partitioned by the transaction date and ordered by the transaction time in descending order, I’d rank transactions for each day.”

17. How would you evaluate the effectiveness of a new onboarding process in Jira?

This question gauges your ability to design and evaluate experiments.

How to Answer

Outline your approach and mention the key metrics you’d track and the reasons behind your assumptions.


“Key metrics would include user retention rates to see if more users stick with Jira, time to first key action to assess if users are becoming productive more quickly, and user satisfaction scores from post-onboarding surveys. Comparing these metrics between the two groups would provide a clear picture of the new onboarding procedure’s impact.”

18. A product manager decides to add threading to comments on group posts. We see comments per user increase by 10% but posts go down 2%. Why would that happen?

Atlassian values insights into how feature modifications affect overall engagement and content creation within collaboration tools.

How to Answer

Follow this approach to answer such questions: 1) Ask clarifying questions; 2) Assess requirements; 3) Present your solution; 4) Create a validation plan to assess and iterate the solution for continuous improvement.


“The introduction of comment threading likely encouraged more in-depth discussions within existing posts, increasing the engagement per post as users reply to comments more frequently. This could lead to a decrease in overall posts because users are spending more time writing in the comments rather than creating new posts. To further understand this shift, looking at metrics like time spent on the platform, number of replies per comment, and overall user satisfaction with the feature would be useful. It’s also possible that the quality of engagement has improved, with users preferring to contribute to existing discussions rather than starting new ones.”

19. How would you build a recommendation system for Confluence users to suggest the most relevant documents they should read next?

Atlassian aims to improve user engagement and productivity by making it easier for users to find relevant content.

How to Answer

Emphasize the importance of a personalized approach considering the user’s history and preferences.


“I’d start by looking at user interaction data, including which documents users have read, edited, or commented on. Using collaborative filtering, we can identify patterns in user behavior to recommend documents read by users with similar interactions. Additionally, content-based filtering could analyze the content of the documents themselves, using natural language processing to understand topics and suggest documents with similar content. A hybrid approach combining both methods would ensure personalized recommendations that reflect both the user’s interest and the content’s relevance. Regularly updating the model with new data and user feedback would help refine and improve the recommendations over time, enhancing user engagement on the platform.”

20. Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? Give an example of the tradeoffs between the two.

This question assesses your understanding of ensemble methods and your ability to choose the appropriate algorithm based on a project’s specific needs.

How to Answer

Highlight the key differences and provide relevant examples of how you would employ each method.


“Bagging, like in a random forest, is robust against overfitting and works well with complex datasets. However, it might not perform as well when the underlying model is overly simple. Boosting, exemplified by algorithms like XGBoost, often achieves higher accuracy but can be prone to overfitting, especially with noisy data. It’s also typically more computationally intensive.”

How to Prepare for a Data Scientist Interview at Atlassian

Here are some tips to help you excel in your interview.

Understand the Role and Responsibilities

Research the specific role you’re applying to. Understand its key responsibilities and the skills required. Visit Atlassian’s careers page for tips on preparing for their interview.

Understand the Fundamentals

Brush up on core data science topics like statistics, machine learning algorithms, data preprocessing, and model evaluation. Be comfortable with Python, SQL, and the Python libraries commonly used for machine learning and statistical modeling, like pandas, scikit-learn, and TensorFlow.

For further practice, refer to our popular guide on quantitative interview questions.

If you need additional guidance, we also offer our tailored data science learning path covering core topics and practical applications.

Prepare Behavioral Interview Answers

Soft skills such as collaboration and adaptability are paramount to succeeding in any job, especially data science roles, where you’ll need to coordinate with teams from non-technical backgrounds and stakeholders from different geographies.

To test your current preparedness for the interview process, try a mock interview to improve your communication skills.

Frequently Asked Questions

What is the average salary for a data scientist role at Atlassian?


Average Base Salary


Average Total Compensation

Min: $123K
Max: $200K
Base Salary
Median: $153K
Mean (Average): $156K
Data points: 21
Min: $115K
Max: $362K
Total Compensation
Median: $237K
Mean (Average): $234K
Data points: 11

View the full Data Scientist at Atlassian salary guide

The average base salary for a data scientist at Atlassian is $155,625, considerably higher than the average salary for data science roles in the US.

Check out our comprehensive Data Scientist Salary Guide for more insights into the salary range of a data scientist at various companies, segmented by city, seniority, and company.

Where can I read more discussion posts on the Atlassian Data Scientist role on Interview Query?

Here is our discussion board, where our members talk about their Atlassian interview experience. You can also use the search bar to look up data scientist interview experiences in other firms to gain more insight into interview patterns.

Are there job postings for Atlassian Data scientist roles on Interview Query?

We have jobs listed for data science roles in Atlassian, which you can apply for directly through our job portal. You can also look at similar roles relevant to your career goals and skill set.


Succeeding in an Atlassian data science interview requires a strong foundation in SQL and ML algorithms, the ability to apply them to real-world problems, and the capacity to communicate your findings to business stakeholders.

If you’re considering opportunities at other tech companies, check out our company interview guides. We cover a range of companies, including GoogleIBM, Apple, and more.

For further data-related roles at Atlassian, explore our guides for data engineerdata scientist, and other roles in our main Atlassian interview guide.

With diligent preparation and a solid interview strategy, you can confidently approach the interview and showcase your potential as a valuable employee at Atlassian. Check out more of our content here at Interview Query, and we hope you land your dream role soon!