Senior Data Scientist, Experimentation id-9324

May 23, 2025

Overview

Working at Atlassian

Atlassians can choose where they work – whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

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Your future team

The Experimentation Data Science team sits within the in the Atlassian Corporate Engineering Organization. The team is tasked with pursuing opportunities to increase the velocity and value of experiments in Atlassian. We work cross-functionally with stakeholders in GTM, growth, product, data engineering, and analytics platform teams. We provide experimentation expertise for experimenting at scale and help teams experiment successfully by delivering training, tools, and analyses to go from idea to decision as quickly as possible. The teams we work with are split geographically across the US and Australia. We are highly nimble, with a focus on velocity between theory and practice, and laser-focused on business impact.

Responsibilities

What you’ll do

As a Senior Experimentation Data Scientist, you will help drive the experimentation practices and analyses, collaborating with business, engineering, and analytics teams, to enable trustworthy decisions at scale. You will help Atlassians use novel ways (sprt, stratified random sampling, etc.,) to increase the speed of the experimentation lifecycle. Develop ways to ensure that experiment decision rigor is the highest standard possible.

Qualifications

On the first day, we'll expect you to have

Master or PhD in a quantitative subject (Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience)

3+ years of related industry experience in the data science and experimentation domain

Experience building and scaling experimentation practices, statistical methods, and tools in a large scale organization

Experience with multiple experiment statistical methods (causal inference, multi-arm bandits. reinforcement learning, synthetic data and experimentation, non-parametric methods, etc.,)

Expertise in SQL, familiarity with Python, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)

Ability to communicate and explain data science and experimentation concepts to diverse audiences, craft a compelling story.

Focus on business practicality and the 80/20 rule; very high bar for output quality, but recognize the business benefit of "having something now" vs "perfection sometime in the future"

Agile development mindset, appreciating the benefit of constant iteration and improvement

Desirable Qualifications

Experience working in a consumer or B2C space for a SaaS product provider, or the enterprise/B2B space

Familiarity working with Engineering, Product and Growth teams.

Excelling in solving ambiguous and complex problems, being able to navigate through uncertain situations, breaking down complex challenges into manageable components and developing innovative solutions

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