Mark Scarr: Elevating Atlassian’s Data Science as a Senior Director

**Title: Mark Scarr: The Future of Machine Learning at Atlassian**

**Introducing Mark Scarr and his Role at Atlassian**
Mark Scarr, the Senior Director of Data Science at Atlassian, leads the Core Machine Learning Team. With years of experience in the tech industry, Scarr has a comprehensive understanding of both B2B and B2C spaces. He shares insights on the team’s projects, Atlassian’s use cases, cloud migration, and future plans.

**The Scope of Atlassian’s ML Team and Production Models**
Atlassian’s ML team, though relatively small, has a broad scope and collaborates with multiple teams across the organization. Their projects involve various Atlassian products such as Trello, Confluence, and Jira. The team has worked on recommendation engines, propensity modeling, and is now exploring generative AI.

**Machine Learning Use Cases Across Atlassian’s Product Portfolio**
One primary use case the team focuses on is performance marketing, specifically in keyword harvesting and bidding optimization. They employ NLP techniques, including semantic similarity and clustering, to enhance keyword pools. Additionally, Atlassian emphasizes modeling customer lifetime value, which plays a role in bidding predictions and informs decision-making.

**The Impact of Cloud Migration on Atlassian and the ML Team**
Cloud migration has revolutionized Atlassian’s business opportunities, particularly due to the accessibility of richer data sets. The ML team benefits from this migration as they can train models more efficiently and effectively. Atlassian is committed to cloud solutions across its product portfolio to capitalize on the advantages brought by ML and data richness. Instrumentation plays a crucial role in capturing new data points, making it an area of focus for the team.

**Aligning Model Metrics with Business Results**
Measuring the impact of ML models on business metrics is critical. Atlassian closely collaborates with business and product leads, ensuring the metrics they aim to improve align with the company’s overall objectives. Engagement models vary depending on the project, but the team emphasizes the involvement of business stakeholders throughout the process. By understanding the business’s needs and building solutions accordingly, the team ensures mutual satisfaction and success.

**Collaboration with Business and Product Leads in Tying Metrics to Results**
Atlassian adopts a flexible engagement model, tailoring their approach to the specific project and team they are working with. Close collaboration with analytics experts and involvement of business stakeholders drive their projects. This ensures that the solutions developed are precisely what the business requires, leading to favorable outcomes for both the ML team and the stakeholders. Scarr emphasizes the importance of a collaborative approach in building models and delivering impactful results.

Overall, Mark Scarr and his team at Atlassian demonstrate their commitment to driving innovation and leveraging ML to enhance various aspects of the business. Their focus on collaboration, data richness, and alignment with business metrics sets the stage for a promising future within the realm of machine learning.

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