Principal Data Scientist - Experimentation Interview questions at Atlassian

As someone who has gone through the interview process for the Principal Data Scientist - Experimentation role at Atlassian, I’d like to share a detailed account of my questions. This position focuses on advanced experimentation methods and data-driven decision-making to optimize products like Jira, Trello, and other Atlassian offerings. Here’s a comprehensive overview of what you can expect during the interview process, along with examples and key insights.

Interview Process Overview

Atlassian’s interview process for the Principal Data Scientist - Experimentation role is comprehensive, multi-stage, and designed to assess both your technical expertise and ability to drive business decisions through data. The process is structured as follows:

1. Recruiter Screening

• Duration: 30-45 minutes
• Purpose: The recruiter will get a high-level overview of your questions, your interest in the role, and your motivations for applying. This is also a chance to discuss the role in detail and align your background with the position’s requirements.

What to expect:

Expect questions like:

• “Why are you interested in the experimentation field at Atlassian?”
• “Can you tell me about a recent project where you designed and ran an A/B test?”
• “How do you prioritize experiments when working with cross-functional teams?“

2. Technical Screening

• Duration: 1 hour
• Format: This is a live coding and problem-solving round where you’ll be tested on your ability to handle data and design experiments. The focus is on A/B testing, statistical methods, and experimental design.

Example Questions:

• “You’re tasked with improving user engagement on Trello by introducing a new feature. How would you design an A/B test to evaluate its effectiveness?”

• “How would you handle situations where the results of an experiment show statistically insignificant results?”

• “Write a SQL query to identify users who participated in an A/B test and segment them based on the groups they were assigned to.”

• Preparation Tip: You should be comfortable with tools like Python or R, as well as SQL, to conduct data analysis. Be ready to explain how you would set up a proper experimental design, control for confounding variables, and ensure the integrity of your experiments.

3. Experimental Design Deep Dive

• Duration: 1-1.5 hours
• Format: This round will focus heavily on your ability to design, implement, and analyze controlled experiments, particularly A/B tests. Expect to discuss your questions with hypothesis testing, power analysis, and statistical significance.

Example Topics and Questions:

• “How would you approach an experiment that has multiple variants and needs to account for both user behavior and product feedback?”

• “Can you explain the concept of multi-arm bandit testing and when it would be preferable over A/B testing?”

• “Describe a time when an A/B test result was inconclusive. How did you proceed, and what changes did you make to the experimental design?”

• “What statistical techniques do you use to ensure that the results of your experiments are valid and robust?”

• Preparation Tip: In-depth knowledge of experimental design is crucial. Be prepared to walk through the steps you would take when designing a controlled experiment, from identifying the hypothesis to collecting and analyzing data. You should also be able to explain the nuances of statistical methods like T-tests, confidence intervals, and Bayesian inference.

4. Behavioral Interview & Case Study

• Duration: 1 hour
• Format: This round will focus on how you approach problem-solving, communicate complex data findings, and collaborate with cross-functional teams. You’ll be given a case study to solve, typically related to improving a product or addressing a business challenge with data.

Example Case Study Questions:

• “Suppose you’re asked to evaluate the impact of a new feature on Jira’s user engagement. How would you design the experiment, and what metrics would you track to measure success?”

• “How would you evaluate the effect of Trello’s paid plans on conversion rates using historical experiment data?”

• “Imagine you have a dataset from an experiment with a high variance. How would you identify the sources of variance and adjust your analysis accordingly?”

• Preparation Tip: This round is about demonstrating how you break down a business problem and use data science to drive decisions. Practice with real-world case studies and be prepared to talk about your past questionss solving similar business challenges.

5. Final Interview with Leadership

• Duration: 45 minutes to 1 hour
• Format: This is a conversation with senior leadership and other stakeholders to assess cultural fit and alignment with the team’s strategic vision. The focus is on how you can lead data-driven experimentation efforts and contribute to Atlassian’s long-term goals.

Example Questions:

• “How do you ensure that your experimental results align with the broader business strategy?”

• “Describe how you handle disagreements or conflicts in an experiment’s design or analysis when working with product managers or engineers.”

• “What are your thoughts on the role of experimentation in a company’s product development cycle?”

• Preparation Tip: Focus on leadership and your ability to drive experimentation strategies at scale. Atlassian values collaborative leadership and expects senior data scientists to influence decisions, mentor junior team members, and present findings to business stakeholders.

Key Skills and Concepts Tested

1. Statistical and Experimental Design Expertise

Understanding the nuances of hypothesis testing, statistical significance, and the application of various methods for experimentation (e.g., A/B testing, multi-arm bandit, Bayesian approaches) is critical. Be prepared to explain why you choose one method over another based on the context of the experiment.

2. Data Analysis and Coding Skills

Proficiency in Python or R for statistical analysis, as well as strong SQL skills for data extraction and manipulation, is essential. You may be asked to analyze data from past experiments or run simulations to demonstrate your ability to work with large datasets.

3. Communication and Leadership Skills

As a principal data scientist, your ability to present complex results in a clear, actionable way is key. You will be expected to influence product development and guide teams based on your findings. Expect to answer questions about leadership, collaboration, and mentoring junior data scientists.

4. Business Acumen

At Atlassian, data scientists need to align their experimentation efforts with business goals. You will need to demonstrate that you understand how experimentation impacts product development, user engagement, and revenue generation. Be ready to discuss how you’ve contributed to business outcomes in your previous roles.

Preparation Tips

1. Master Experimental Design

Ensure you are well-versed in both theoretical and practical aspects of designing experiments, from the initial hypothesis to analyzing results. Be prepared to discuss complex cases, such as how you would manage large-scale experiments or multi-variable testing.

2. Review Statistical Methods

Be sure you understand statistical significance, power analysis, and how to handle challenges like sample size determination or false positives.

3. Practice Coding

Be ready to demonstrate your coding skills in Python, R, or SQL. Practicing A/B testing code, data wrangling, and statistical analysis will be helpful for the technical rounds.

4. Prepare for Leadership Questions

Atlassian values leadership and strategic thinking. Think about your past questionss leading teams and managing complex experiments, especially how you’ve influenced product decisions using data.