Hybrid Data Scientist – Recommendation

at Square Enix

• Job title: Data Scientist – Recommendation
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Docker, Kubernetes, Python, PyTorch, Scikit-Learn, SQL, Tensorflow
• Location requirements: Hybrid, London, United Kingdom

About the role

• Data Scientist focusing on recommendation systems at Square Enix. Building and integrating machine learning strategies to enhance marketing and sales opportunities.

Responsibilities

• Design and implement recommendation engines using collaborative filtering, content-based methods, and rule-based approaches, tailored to both new releases and catalogue titles.
• 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.

Requirements

• Demonstrable current proficiency in applied mathematics relevant to machine learning and business analytics (e.g., A-level Mathematics 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, content-based 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.
• Experience integrating predictive models (e.g., awareness, intent, forecasted sales) into recommendation logic.
• Familiarity with probabilistic modeling libraries (e.g., PyMC, Stan) and causal inference frameworks (e.g., DoWhy, EconML).
• Experience designing and evaluating personalized marketing interventions.
• Experience working with marketing or e-commerce data.