Product Management jobs in United Kingdom

Our Data Science Analytics and Enablement (DSAE) team inspires PlayStation to make impactful, customer-centric decisions through seamless integration of data.

Currently there are over 100 people in the global DSAE team, including data science, data governance and analytics professionals. We work closely with engineering and product management teams to deliver data products, insight, predictive analytics, and data visualisation.

Data Scientists in the DSAE team apply sophisticated analytical methods and tools to drive value from the data through methods such as customer segmentation, predictive modelling, forecasting and other algorithms. Our work delivers an in-depth understanding of consumer behaviours and supports multi-channel targeting and personalisation.

We’re seeking a motivated and collaborative Data Scientist to join our CLV & Next Best Action / Personalisation team, which builds models that help PlayStation understand and influence the value, growth, and engagement of our global player base.

By connecting player behaviours to long-term outcomes, our work creates the intelligence that underpins Customer Lifetime Value (CLV) modelling and guides PlayStation’s Next Best Action and Personalisation strategies. You’ll help shape how PlayStation invests in, retains, and delights its players - and advance our use of modern machine learning at global scale.

This is an exciting opportunity to develop your skills in modern machine learning while contributing to high-impact projects that directly influence how PlayStation engages and retains its players.

• Build and validate models for use cases such as churn prediction, purchase propensity, CLV modelling, and personalised engagement.
• Work with sequential and behavioural data, applying machine learning approaches (e.g., gradient boosting, embeddings, sequence models) to capture player behaviour and growth.
• Partner with commercial, finance, and lifecycle teams to translate business needs into modelling hypotheses that inform engagement strategies.
• Collaborate with analytics and engineering colleagues to ensure models are efficient, robust, and scalable.
• Communicate findings clearly to both technical and non-technical partners, ensuring insights drive measurable outcomes.
• Grow your expertise in modern ML approaches (transformers, representation learning, sequence-to-sequence modelling) through high-visibility projects.

You’re curious, analytical, and eager to grow your skills in a commercial data science environment. You bring strong fundamentals in modelling and data manipulation, and you’re motivated by the challenge of applying these to impactful business problems.

• Strong analytical and critical thinking skills, with a passion for understanding customer behaviour and enabling data-driven growth.
• Practical experience with predictive modelling, such as churn, propensity, segmentation, or value modelling.
• Proficiency in Python and SQL, with fluency in common data science libraries.
• Ability to work with large datasets to uncover actionable insights that influence marketing, engagement, or retention strategies.
• Interest or early experience in deep learning approaches — such as transformers, embeddings, or sequence models — with motivation to learn and apply them.
• A collaborative attitude, with the ability to work in cross-functional teams.
• A strong academic background (typically with a Master’s in a technical or mathematical field) or equivalent professional experience.

• Experience with A/B testing or experimentation frameworks.
• Familiarity with workflow orchestration tools (e.g., Airflow), feature stores, or visualisation platforms (e.g., Tableau, Domo).
• Exposure to gaming, e-commerce, or subscription-based business models.
• Proven experience of analysing and modelling with “big data” technologies.
• A personal interest in gaming and entertainment.

• Discretionary bonus opportunity
• Hybrid Working (within Flexmodes)
• Private Medical Insurance
• Dental Scheme
• 25 days holiday per year
• On Site Gym
• Subsidised Café
• Free soft drinks
• On site bar
• Access to cycle garage and showers