Melbourne, Victoria, Australia
Data analytics professional with 6+ years of experience driving growth and better decision-making across finance, global sports media, and luxury retail. I focus on helping teams move beyond static reporting by working with stakeholders to embed analytics and machine learning into day-to-day operational and commercial decisions. At AustralianSuper, I worked within the finance function to develop and operationalise machine-learning models that supported forecasting and financial decision-making. Previously at the NBA, my work informed sales and marketing strategy, contributing to a 37% increase in League Pass revenue and a 44% growth in subscribers over three years, while earlier roles at Van Cleef & Arpels and Christie’s focused on customer insights, performance analysis, and data-backed commercial decision-making.
Developed and applied machine learning models on financial data to support forecasting improvements, detect anomalies, and uncover operational patterns within the Finance function, helping reduce manual review and improve forecast visibility. I also supported the development of Power BI dashboards to surface insights and enable ongoing monitoring for stakeholders. Key work included time-series forecasting (ARIMA), anomaly detection (Isolation Forest), and clustering (K-means). I presented three practical machine learning use cases to senior leaders, including the Head of Strategy & Transformation, Head of Data Strategy & AI, and Head of Finance. All models were delivered with clear documentation and reproducible pipelines to ensure effective handover, usability, and continuity beyond the internship.
Working at the NBA combined my long-standing passion for basketball with data analytics. As a Data Analyst, I supported Global Media Distribution (Sales) and Marketing teams across APAC, delivering insights to drive League Pass growth, audience engagement, retention, and content strategy. I partnered closely with Sales, CRM, Social Media, and Content teams, owning analytics projects end-to-end, from problem framing and data extraction to insight delivery and stakeholder recommendations. Key initiatives included leading a machine learning–based customer segmentation project to inform acquisition and retention strategies, and analyzing and executing a season-long local commentary campaign in Thailand, translating engagement data into actionable recommendations that increased subscriber acquisition and revenue.
Within the Retail team, I supported the CRM Manager while partnering closely with the Richemont Group Analytics team, contributing to reporting and analytics initiatives across retail and customer performance. I gained hands-on experience building dashboards and automating recurring reports, helping streamline insight delivery and reduce manual reporting effort. This exposure deepened my interest in analytics beyond traditional reporting and motivated me to further develop my Python and SQL skills to perform more advanced analysis. I regularly presented findings to CRM and retail stakeholders, learning that impact comes not only just from uncovering insights but also from communicating them clearly and persuasively by framing analysis around business context, key decisions, and recommended actions. This strengthened my ability to translate data into clear narratives that drive alignment, buy-in, and action.
This was my first role out of university and my introduction to working with data in a business context. I analyzed client transaction data (bidding, buying, and consigning) to support art specialists and advisors in understanding client behavior and informing client strategy. Through this role, I built a strong foundation in data analysis and developed a lasting interest in using data to drive commercial decision-making.