Principal Data Scientist
Atlassian
Principal Data Scientist
Job Description
Principal Data Scientist
📋 Job Overview
As a Principal Data Scientist at Atlassian, you will leverage data to drive insights and shape strategies for AI-powered capabilities and consumption-based monetization. You will collaborate with cross-functional teams to solve business challenges and use data to inform product development and pricing strategies.
📍 Location: Sydney, Australia
🏢 Category: Analytics & Data Science
📅 Posted: 2025-11-30 08:18 PM
🎯 Key Responsibilities
- Collaborate on a variety of product and business problems with a diverse set of cross-functional partners and become a trusted strategic partner through the structure and clarity of your work.
- Apply technical expertise with quantitative analysis, experimentation, and the presentation of data to develop strategies for our business and help solve the business's biggest challenges.
- Focus on developing hypotheses through analytical approaches, different methodologies, frameworks, and technical approaches to test them.
- Analyze real-world use cases and consumption patterns to inform pricing, forecasting, and elasticity modeling.
- Partner with cross-functional teams to inform, influence, and execute strategy decisions.
- Identify and measure the success of product efforts through forecasting and monitoring of key product metrics to understand trends.
- Use data to shape platform development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Atlassian.
- Use storytelling techniques to communicate insights broadly, including to company leadership teams.
✅ Required Qualifications
- 8+ Years of Experience in Data Science or related fields
- Proficiency in SQL AND another data manipulation programming language (e.g Python, R)
- Expertise in modelling and forecasting timeseries
- Expertise at telling stories with data and familiarity with at least one visualization tool (e.g. Tableau, R-Shiny, Microstrategy, SAP Business Objects, Looker, etc.)
- Expertise in applying a broad variety of statistical concepts (e.g. regressions, A/B tests, clustering, probability) to business problems and a strong sense of when to adapt/combine approaches to suit the problem at hand
- Experience driving projects or programs which have had proven impact on business strategy and performance
- A proven track record for crafting analysis into well-written content
- Experience advising, presenting to and serving as a thought partner to senior executives
⭐ Preferred Qualifications
- 10+ Years of Experience in Data Science or related fields
- Engineering and/or integration background
- An understanding of the SaaS business model and key metrics
🛠️ Required Skills
- SQL
- Python
- R
- timeseries modelling
- forecasting
- data visualization
- Tableau
- R-Shiny
- Microstrategy
- SAP Business Objects
- Looker
- statistical concepts
- regressions
- A/B tests
- clustering
- probability
- storytelling
- quantitative analysis
- experimentation
🎁 Benefits & Perks
- health and wellbeing resources
- paid volunteer days
Locations
- Sydney, Australia
Salary
Estimated Salary Rangemedium confidence
200,000 - 280,000 AUD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- SQLintermediate
- Pythonintermediate
- Rintermediate
- timeseries modellingintermediate
- forecastingintermediate
- data visualizationintermediate
- Tableauintermediate
- R-Shinyintermediate
- Microstrategyintermediate
- SAP Business Objectsintermediate
- Lookerintermediate
- statistical conceptsintermediate
- regressionsintermediate
- A/B testsintermediate
- clusteringintermediate
- probabilityintermediate
- storytellingintermediate
- quantitative analysisintermediate
- experimentationintermediate
Required Qualifications
- 8+ Years of Experience in Data Science or related fields (experience)
- Proficiency in SQL AND another data manipulation programming language (e.g Python, R) (experience)
- Expertise in modelling and forecasting timeseries (experience)
- Expertise at telling stories with data and familiarity with at least one visualization tool (e.g. Tableau, R-Shiny, Microstrategy, SAP Business Objects, Looker, etc.) (experience)
- Expertise in applying a broad variety of statistical concepts (e.g. regressions, A/B tests, clustering, probability) to business problems and a strong sense of when to adapt/combine approaches to suit the problem at hand (experience)
- Experience driving projects or programs which have had proven impact on business strategy and performance (experience)
- A proven track record for crafting analysis into well-written content (experience)
- Experience advising, presenting to and serving as a thought partner to senior executives (experience)
Preferred Qualifications
- 10+ Years of Experience in Data Science or related fields (experience)
- Engineering and/or integration background (experience)
- An understanding of the SaaS business model and key metrics (experience)
Responsibilities
- Collaborate on a variety of product and business problems with a diverse set of cross-functional partners and become a trusted strategic partner through the structure and clarity of your work.
- Apply technical expertise with quantitative analysis, experimentation, and the presentation of data to develop strategies for our business and help solve the business's biggest challenges.
- Focus on developing hypotheses through analytical approaches, different methodologies, frameworks, and technical approaches to test them.
- Analyze real-world use cases and consumption patterns to inform pricing, forecasting, and elasticity modeling.
- Partner with cross-functional teams to inform, influence, and execute strategy decisions.
- Identify and measure the success of product efforts through forecasting and monitoring of key product metrics to understand trends.
- Use data to shape platform development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Atlassian.
- Use storytelling techniques to communicate insights broadly, including to company leadership teams.
Benefits
- general: health and wellbeing resources
- general: paid volunteer days
Tags & Categories
Principal Data Scientist
Atlassian
Principal Data Scientist
Job Description
Principal Data Scientist
📋 Job Overview
As a Principal Data Scientist at Atlassian, you will leverage data to drive insights and shape strategies for AI-powered capabilities and consumption-based monetization. You will collaborate with cross-functional teams to solve business challenges and use data to inform product development and pricing strategies.
📍 Location: Sydney, Australia
🏢 Category: Analytics & Data Science
📅 Posted: 2025-11-30 08:18 PM
🎯 Key Responsibilities
- Collaborate on a variety of product and business problems with a diverse set of cross-functional partners and become a trusted strategic partner through the structure and clarity of your work.
- Apply technical expertise with quantitative analysis, experimentation, and the presentation of data to develop strategies for our business and help solve the business's biggest challenges.
- Focus on developing hypotheses through analytical approaches, different methodologies, frameworks, and technical approaches to test them.
- Analyze real-world use cases and consumption patterns to inform pricing, forecasting, and elasticity modeling.
- Partner with cross-functional teams to inform, influence, and execute strategy decisions.
- Identify and measure the success of product efforts through forecasting and monitoring of key product metrics to understand trends.
- Use data to shape platform development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Atlassian.
- Use storytelling techniques to communicate insights broadly, including to company leadership teams.
✅ Required Qualifications
- 8+ Years of Experience in Data Science or related fields
- Proficiency in SQL AND another data manipulation programming language (e.g Python, R)
- Expertise in modelling and forecasting timeseries
- Expertise at telling stories with data and familiarity with at least one visualization tool (e.g. Tableau, R-Shiny, Microstrategy, SAP Business Objects, Looker, etc.)
- Expertise in applying a broad variety of statistical concepts (e.g. regressions, A/B tests, clustering, probability) to business problems and a strong sense of when to adapt/combine approaches to suit the problem at hand
- Experience driving projects or programs which have had proven impact on business strategy and performance
- A proven track record for crafting analysis into well-written content
- Experience advising, presenting to and serving as a thought partner to senior executives
⭐ Preferred Qualifications
- 10+ Years of Experience in Data Science or related fields
- Engineering and/or integration background
- An understanding of the SaaS business model and key metrics
🛠️ Required Skills
- SQL
- Python
- R
- timeseries modelling
- forecasting
- data visualization
- Tableau
- R-Shiny
- Microstrategy
- SAP Business Objects
- Looker
- statistical concepts
- regressions
- A/B tests
- clustering
- probability
- storytelling
- quantitative analysis
- experimentation
🎁 Benefits & Perks
- health and wellbeing resources
- paid volunteer days
Locations
- Sydney, Australia
Salary
Estimated Salary Rangemedium confidence
200,000 - 280,000 AUD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- SQLintermediate
- Pythonintermediate
- Rintermediate
- timeseries modellingintermediate
- forecastingintermediate
- data visualizationintermediate
- Tableauintermediate
- R-Shinyintermediate
- Microstrategyintermediate
- SAP Business Objectsintermediate
- Lookerintermediate
- statistical conceptsintermediate
- regressionsintermediate
- A/B testsintermediate
- clusteringintermediate
- probabilityintermediate
- storytellingintermediate
- quantitative analysisintermediate
- experimentationintermediate
Required Qualifications
- 8+ Years of Experience in Data Science or related fields (experience)
- Proficiency in SQL AND another data manipulation programming language (e.g Python, R) (experience)
- Expertise in modelling and forecasting timeseries (experience)
- Expertise at telling stories with data and familiarity with at least one visualization tool (e.g. Tableau, R-Shiny, Microstrategy, SAP Business Objects, Looker, etc.) (experience)
- Expertise in applying a broad variety of statistical concepts (e.g. regressions, A/B tests, clustering, probability) to business problems and a strong sense of when to adapt/combine approaches to suit the problem at hand (experience)
- Experience driving projects or programs which have had proven impact on business strategy and performance (experience)
- A proven track record for crafting analysis into well-written content (experience)
- Experience advising, presenting to and serving as a thought partner to senior executives (experience)
Preferred Qualifications
- 10+ Years of Experience in Data Science or related fields (experience)
- Engineering and/or integration background (experience)
- An understanding of the SaaS business model and key metrics (experience)
Responsibilities
- Collaborate on a variety of product and business problems with a diverse set of cross-functional partners and become a trusted strategic partner through the structure and clarity of your work.
- Apply technical expertise with quantitative analysis, experimentation, and the presentation of data to develop strategies for our business and help solve the business's biggest challenges.
- Focus on developing hypotheses through analytical approaches, different methodologies, frameworks, and technical approaches to test them.
- Analyze real-world use cases and consumption patterns to inform pricing, forecasting, and elasticity modeling.
- Partner with cross-functional teams to inform, influence, and execute strategy decisions.
- Identify and measure the success of product efforts through forecasting and monitoring of key product metrics to understand trends.
- Use data to shape platform development, quantify new opportunities, identify upcoming challenges, and ensure the products we build bring value to people, businesses, and Atlassian.
- Use storytelling techniques to communicate insights broadly, including to company leadership teams.
Benefits
- general: health and wellbeing resources
- general: paid volunteer days
Tags & Categories