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.

Responsibilities

• Lead the design, development, and deployment of scalable, reliable, and high-performance data and ML pipelines for search, powering data acquisition across model training, evaluation and experimentation for team.
• Architect and optimize data infrastructure for ingesting, processing, and serving large-scale, high-velocity search and experimentation data.
• Develop and productionize ML models for search ranking, relevance, and personalization, ensuring robust offline and online experimentation.
• Drive best practices in data quality, governance, reproducibility, and operational excellence for ML and data engineering workflows.
• Collaborate with cross-functional teams (data scientists, engineers, product managers) to define data schemas, metrics, and logging standards for experimentation and search analytics.
• Mentor and guide engineers across teams, raising the bar for technical excellence, experimentation rigor, and system reliability.
• Champion MLOps and DataOps practices to streamline model deployment, monitoring, and lifecycle management.
• Continuously improve experimentation velocity by building frameworks and tools for A/B testing, causal inference, and rapid iteration

Qualifications

• Master or PhD in a quantitative subject (Statistics, Mathematics, Computer Science, Operations Research, or relevant work experience)
• 5+ years of related industry experience in the data science domain
• Expertise in Python or Java with and the ability to write performant production-quality code, familiarity with SQL, knowledge of Spark and cloud data environments (e.g. AWS, Databricks). Experienced in building production-level data engineering pipelines.
• Experience building and scaling machine learning models in business applications using large amounts of data. Experienced in search domain.
• Ability to communicate and explain data science 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

Benefits & Perks Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits . About Atlassian At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.

We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.

To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.

To learn more about our culture and hiring process, visit go.atlassian.com/crh .