Atlassian can hire people in any country where we have a legal entity, assuming candidates have eligible working rights and a sufficient timezone overlap with their team. As our offices re-open, Atlassians can choose to work remotely or return to an office, unless it’s necessary for the role to be performed in the office. Interviews and onboarding are conducted virtually, a part of being a distributed-first company.

With a sufficient timezone overlap with the team, we’re able to hire eligible candidates for this role from any location in Australia and/or New Zealand. If this sparks your interest, apply today and chat with our friendly Recruitment team further.

In this role, you'll get to:

• Develop machine learning algorithms to predict customer lifetime value and drive business applications using these predictions
• Construct and refine AI algorithms to optimise our marketing and onboarding funnels
• Employ behavioural data and insights to intelligently customize the user journey, boost engagement and purchase rates and increase our number of active users
• Work with our seriously large volume of analytics data to understand insightful trends and behaviours
• Craft machine learning and predictive models to drive intelligent product features

On your first day, we'll expect you to have:

• Bachelor or higher degree (or equivalent) in a quantitative subject (statistics, mathematics, computer science, engineering or physics)
• Industry experience in the data science or machine learning domain
• Development experience in a programming language, Python is strongly preferred
• Expertise in SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks)
• Experience developing, building and scaling machine learning models in business applications using large amounts of data. Previous experience in customer lifetime value, churn and propensity modelling is highly regarded
• Ability to communicate and explain data science and machine learning concepts to diverse audiences and craft a compelling story
• Focus on business practicality and the 80/20 rule; very high bar for output quality but recognise the business benefit of “having something now” vs “perfection sometime in the future”
• Agile development approach, appreciating the benefit of review, constant iteration and improvement within a collaborative team environment

It's great, but not required, if you have:

• Experience working in an enterprise or B2B space for a SaaS product provider, as well as the consumer or B2C space
• Familiarity working with go-to-market (GTM), Strategy, Product and Growth teams
• Knowledge of advanced machine learning and deep learning methods applied to digital user behaviours
• Knowledge of A/B experimentation