Data Scientist - Forecasting

sonyinteractiveentertainmentglobal · Londonmid

In short

▸Desarrollar modelos de machine learning para predecir churn, propensión a comprar y valor de cliente en jugadores.
▸Trabajar con grandes volúmenes de datos de comportamiento para generar insights que mejoren la retención y monetización.
▸Destacar por liderar problemas desde la definición hasta la entrega con impacto comercial real.

Strong communication skills in English are required.

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What they ask for

✓Experiencia en modelos predictivos (churn, propensión, segmentación, valor) en entornos comerciales.
✓Capacidad de liderar problemas desde su definición hasta la entrega con autonomía.
✓Dominio de Python y SQL, con familiaridad con librerías de ciencia de datos y ML.
✓Conocimiento sólido de técnicas de ML (regresión, modelos de árboles, clustering) y su aplicación práctica.
✓Habilidades de comunicación claras para presentar insights a audiencias técnicas y no técnicas.
✓Colaboración efectiva con equipos multifuncionales (product, ingeniería, comercial, finanzas).

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PythonSQLmachine learningregressiontree-based modelsclusteringdeep learningsequence-based methodsdata science librariesML libraries

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Why Sony Interactive Entertainment? Sony Interactive Entertainment isn’t just the Best Place to Play — it’s also the Best Place to Work. Sony Interactive Entertainment (SIE) is the company behind the PlayStation brand. As a subsidiary of Sony Group Corporation, we’re part of a proud legacy of innovation and excellence. SIE is a dynamic technology company, delivering cutting-edge hardware and network services to more than 100 million people and an entertainment leader, home to some of the most beloved and recognizable intellectual properties (IP) in the world. Our role at SIE is to create and nurture the experiences under the PlayStation brand, a name synonymous with entertainment excellence and creativity. Department overview: At PlayStation, Data Science plays a critical role in shaping how we invest in, retain, and delight our global player base. The CLV team focuses on understanding player behaviour and driving more effective engagement across the player lifecycle — from acquisition and onboarding through to retention, monetisation, and long-term value. As a Data Scientist , you will develop models and insights that help drive more personalised player experiences and inform commercial and product decisions at scale. You will work in cross-functional teams to translate player behaviour into actionable strategies that drive measurable improvements in player engagement and value. This role is ideal for an individual who is comfortable owning problems end-to-end — from framing and modelling through to delivering impact — and partnering with stakeholders to support data-informed decision-making. What you’ll be doing: • Own the development and delivery of machine learning models for use cases such as churn prediction, purchase propensity, store recommendations, customer lifetime value. • Translate business problems into modelling approaches, selecting appropriate methods and features to deliver measurable impact. • Work with large-scale behavioural and transactional data to uncover patterns and opportunities for player growth and engagement. • Collaborate within cross-functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are robust, scalable, and aligned with business needs. • Partner with stakeholders across commercial, finance, and lifecycle teams to support decision-making with data-driven insights. • Clearly communicate findings and recommendations to both technical and non-technical audiences. • Develop and expand your understanding of more advanced modelling approaches (e.g. deep learning and sequence-based methods) as part of solving increasingly complex problems. What we are looking for: You're curious, analytical, and a strong problem-solver, with a structured approach to tackling business problems. You bring strong foundations in modelling and data manipulation, and are motivated by applying these to impactful, commercial problems. • Experience building predictive models (e.g. churn, propensity, segmentation, or value modelling) in a commercial setting. • Ability to independently take a problem from definition through to solution and delivery, demonstrating initiative and ownership. • Proficiency in Python and SQL, and familiarity with common data science and ML libraries. • Solid understanding of machine learning techniques (e.g. regression, tree-based models, clustering) and when to apply them, including how to refine and tune them for real-world problems. • Strong communication and collaboration skills, with the ability to clearly articulate insights and work effectively with cross-functional stakeholders.&nbsp

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