Data Scientist, Square Enix

• The ideal candidate combines technical rigor with business-oriented thinking and thrives in collaborative environments
• Demonstrable current proficiency in applied mathematics relevant to machine learning and business analytics (e.g., A-levelnMathematics with grade A or A+ or equivalent)
• Proficiency in Python and SQL for data analysis and model development
• Strong foundation in statistics, probability, and linear algebra
• Experience with recommender system techniques such as collaborative filtering, contentbased recommendation, and rule-based logic
• Familiarity with ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch)
• Exposure to ML operations, including: Code versioning (e.g., Git), Experiment tracking, and Model deployment and monitoring (e.g., CI/CD pipelines, Vertex AI Pipelines), containerization and deployment tools (e.g., Docker, Kubernetes), cloud computing platforms (e.g., Google Cloud, AWS, Azure)
• Strong delivery mindset, with the ability to work under tight deadlines and consistently drive business impact
• Excellent communication and collaboration skills, with the ability to work across data science, engineering, and business teams

Desirable

• We are seeking a passionate and driven Data Scientist to join our dynamic team. This role focuses on building and improving recommendation systems, monitoring model performance, and conducting deep behavioural analytics to uncover actionable insights
• This role also bridges Recommendation experts and Forecast experts; they will focus on designing machine learning strategies that personalize marketing interventions for long-tail sales opportunities
• Working closely with the Forecast experts, they will integrate predictive models into recommendation logic and evaluate the impact of personalized actions on sustained revenue
• Design and implement recommendation engines using collaborative filtering, contentbased methods, and rule-based approaches, tailored to both new releases and catalogue titles. These solutions are also designed to span multiple categories (HD, MD, MMO) to drive broader cross-sell opportunities
• Integrate forecast outputs (e.g., awareness scores, purchase intent) into recommendation logic to personalize marketing actions
• Develop personalized marketing interventions (e.g., bundles, coupons, content surfacing) aligned with sales schedules and forecasted demand
• Conduct user behavior analysis to uncover actionable insights:
• Path analysis to trace user journeys and identify drop-off points
• Predictive modeling to quantify drivers of engagement and conversion
• Finding cross-sell opportunities across multiple channels and product categories
• Collaborate with the Forecast team to align recommendation strategies with predictive models and business priorities
• Manage and version control codebases (e.g., Git), organize experiments, and improve pipeline robustness
• Communicate findings and recommendations clearly to stakeholders across business and technical teams