_{19th November, 2025}

RSL Queensland (RSLQ) is a dynamic, not-for-profit organisation delivering innovative programs and initiatives that support veterans and their families.  

Our Lotteries department is the proud custodian of the Dream Home Art Union lottery, offering Australians the chance to win multimillion-dollar properties and prizes.

As RSLQ transforms to meet the evolving needs of veterans and their families, we are taking bold steps to increase revenue through diverse teams harnessing technology and data to reshape customer experiences and develop innovative lottery products. 

The Senior Data Scientist supports Lotteries to realise its business goals by providing insights through data science, machine learning and statistical analysis.  

Reporting to the Head of Commercial and Customer Insights and leads our driven and growing Data Science team, key responsibilities of the position include (though are not limited to): 

• Delivering data-driven insights to all Lotteries functions 
• Support the delivery of commercial benefits using advanced statistical and predictive modelling techniques and AI 
• Developing a variety of advanced quantitative methods to demonstrably improve decision-making across Lotteries. 
• Developing robust forecasting methods to predict business performance of the Lotteries division. 

About you:  

• Formally qualified in a quantitative discipline (e.g. data science, statistics, mathematics, actuarial studies, science or engineering)  
• 5+ years of experience in a hands-on data science role (commercial, B2C environment) in partnership with sales, marketing and product teams 
• Delivering actionable customer-centric insights and developing models to predict churn, acquisition, cross-sell, up-sell, next best actions and/or personalisation with robust cross-validation methods.  
• Data storytelling and visualisation skills and the ability to communicate complex analytical, statistical and technical concepts seamlessly to non-technical stakeholders. 
• Expert at statistical modelling, data science and machine learning methods 
• Extensive experience building, interpreting and sharing the results from logistic and multiple linear regression models, survival models and tree-based ensemble methods 
• Experience using model interpretability frameworks (like LIME and SHAP) 
• Python (pandas, scikit-learn, pytorch, polars), SQL, git, jupyter notebooks expertise is a must 

If you are adept at developing and managing predictive model lifecycles in an MLOps environment and leveraging LLMs or AI agents for elevated data science efficiency, your application will be even more competitive.  

For a complete outline of role accountabilities and essential qualifications and experience, refer to our position description.  

If performing advanced data analysis to better understand current business performance and building predictive models to better estimate and drive future business performance is your jam, we want to hear from you. 

Applications close at 5pm AEST Monday 1 December 2025. 

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