Growth Data Scientist
Description
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 foster 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 crafting the future of our hit games.
Responsibilities
- 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.
Requirements
- Degree in Mathematics, Statistics, Economics, Computer Science, or a similar quantitative field, or equivalent experience.
- 2+ years of professional data science experience, with a proven track record of delivering end-to-end data or ML projects.
- Practical experience applying data science 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 presenting 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.
- Collaborative, with excellent collaborator management skills.