Data Scientist – Forecasting

Overview

In this role you will develop ML models to optimise player engagement and monetisation for PlayStation’s global audience. You will join the CLV data science team to translate player behaviour into actionable, measurable strategies, collaborating with engineering, product and commercial stakeholders. You will own problems end-to-end, from framing to delivering impact, and progressively adopt advanced modelling approaches. This is an opportunity to shape personalised experiences at scale within a mission-driven gaming company.

Pay / Benefits

• Discretionary bonus opportunity
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café? Wait: Subsidised Café

Responsibilities

• Develop and deliver ML models for churn prediction, purchase propensity, store recommendations, and customer lifetime value
• Translate business problems into modelling approaches and select appropriate methods
• Work with large-scale behavioural and transactional data to identify growth and engagement opportunities
• Collaborate with cross-functional teams (engineering, product, commercial) to ensure robust, scalable solutions
• Partner with commercial, finance, and lifecycle teams to enable data-driven decision making
• Communicate findings and recommendations clearly to technical and non-technical audiences
• Develop understanding of advanced modelling approaches (e.g., deep learning, sequence models) for complex problems

Key requirements

• Strong foundations in modelling and data manipulation
• Experience building predictive models in a commercial setting (e.g., churn, propensity, segmentation, value modelling)
• Ability to take a problem from definition through to delivery with ownership
• Proficiency in Python and SQL; familiarity with ML libraries
• Solid understanding of ML techniques (regression, tree-based models, clustering) and tuning for real-world use
• Strong communication and collaboration skills with cross-functional stakeholders
• Awareness of modern ML approaches (embeddings, sequence models, deep learning) and willingness to apply them
• Experience with large datasets to generate actionable insights
• Master’s or Ph.D. in Mathematics, Statistics, Computer Science; strong academic background

• Strong problem-solving
• Structured approach to business problems
• Clear communication
• Python
• SQL
• ML libraries (e.g., scikit-learn, PyTorch/TensorFlow familiarity)

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