Growth Data Scientist

Join Aristocrat as a Growth Data Scientist and help us build world-class mobile gaming experiences! This role offers an outstanding opportunity to be at the forefront of driving player acquisition strategies using innovative data science techniques. At Aristocrat, we cultivate an inclusive and collaborative culture, where innovation and excellence are celebrated. You will play a pivotal role in advising our Growth team on budget allocation and channel efficiency, ultimately contributing to the crafting of what lies ahead for our hit games.

This is a hybrid position based in Barcelona, requiring team members to work from our Barcelona office three days per week.

What You'll Do

• Counsel the UA team regarding budget planning and channel tactics based on comprehensive data analysis and modelling.
• Apply statistical analysis, causal inference, machine learning, and time-series modelling to understand user behavior and assess marketing channel efficiency.
• Proactively identify areas of improvement; build and run analyses and models that lead to actionable recommendations.
• Build and maintain forecasting models to support UA planning and decision-making.
• Translate complex analytical findings into clear, compelling insights for both technical and non-technical collaborators.
• Collaborate with Data Analytics and Engineering teams to ensure reliable data foundations and feature availability.
• Learn from and share ideas with a diverse, globally distributed team of data scientists.

What We're Looking For

• Degree in Mathematics, Statistics, Economics, Computer Science, or a similar quantitative field, or equivalent experience.
• Professional data science experience, with a consistent track record of delivering end-to-end data or ML projects.
• Practical experience applying analytical techniques to address real business challenges, ideally in a marketing, growth, or digital environment.
• Experience in gaming or digital entertainment is a strong plus.
• Proficiency in Python and SQL.
• Experience building forecasting and time-series models.
• Familiarity with causal inference methods (e.g. diff-in-diff, regression discontinuity, uplift modelling).
• Knowledge of statistical analysis and experimentation (A/B testing, significance testing).
• Experience with data visualization tools and communicating analytical results clearly.
• Pragmatic approach to problem-solving, favoring practical solutions.
• Comfortable working with ambiguity and switching context as business priorities evolve.
• Curious and proactive, with a deep understanding of problems before jumping to solutions.

If you’re passionate about using data science to build outstanding gaming experiences and are ready to lead data products to success, we want to hear from you. Join Aristocrat and help develop the future of mobile gaming!

This job description may have been reviewed and enhanced using AI-assisted tools to improve clarity, consistency, and inclusivity. All final content, role requirements and hiring decisions remain subject to human review and approval by Aristocrat.

Compensation Philosophy

We offer a comprehensive pay and benefits package designed to stay competitive in the market, support your wellbeing, and recognise your contribution to our success. Our approach is underpinned by a pay-for-performance belief in rewarding individual impact. Your specific compensation package will be determined by factors such as your skills, experience, qualifications, and location.

Depending on your role and location, you may be eligible for annual bonuses and incentives, health and wellbeing benefits, paid time off, retirement plans, insurance coverage, and other local or statutory benefits.

Specific details about compensation and benefits for this position will be discussed during the recruitment process.

- Degree in Mathematics, Statistics, Economics, Computer Science, or similar quantitative field, or equivalent experience. - Professional data science experience with a consistent track record of delivering end-to-end data or ML projects. - Practical experience applying analytical techniques to address real business challenges, ideally in marketing, growth, or digital environments. - Proficiency in Python and SQL. - Experience building forecasting and time-series models. - Familiarity with causal inference methods (e.g., diff-in-diff, regression discontinuity, uplift modelling). - Knowledge of statistical analysis and experimentation (A/B testing, significance testing). - Experience with data visualization tools and communicating analytical results clearly. - Pragmatic approach to problem-solving. - Comfortable working with ambiguity and evolving business priorities.