Senior Data Scientist - CLV & Personalisation in London

Role Overview: Senior Data Scientist on the CLV & Personalisation team shaping player value, engagement, and growth across the lifecycle.

Responsibilities:

• Design and deliver advanced machine learning models to drive player value, engagement, and growth across the lifecycle.
• Own ambiguous problem spaces, defining modelling approaches and breaking down complex business challenges into actionable solutions.
• Partner with senior stakeholders to shape strategies and ensure Data Science drives high-impact decisions.
• Apply advanced ML techniques, including deep learning and sequence modelling, where appropriate to capture complex player behaviours and improve model performance.
• Evaluate and select modelling approaches based on problem context, balancing performance, scalability, and interpretability.
• Work within cross‑functional teams, including engineering, product, and commercial stakeholders, to ensure solutions are scalable, production‑ready, and aligned with long‑term architecture.
• Mentor other data scientists and contribute to best practices across the team.
• Communicate complex modelling approaches, assumptions, and outcomes clearly to senior technical and non‑technical audiences.

Qualifications:

• Proven industry experience delivering impactful, production‑ready ML solutions in areas such as customer value, retention, or personalisation.
• Strong expertise in modern ML approaches, including transformers, embeddings, or sequence‑based models applied to real‑world behavioural data.
• Ability to critically assess when advanced approaches are warranted versus simpler solutions.
• Experience owning projects end‑to‑end, including problem definition, navigating ambiguity, selecting modelling approaches, and delivering impactful solutions.
• Ability to work with large, complex datasets and translate them into scalable solutions.
• Strong stakeholder management and communication skills, with the ability to influence decisions and communicate results persuasively to both technical and non‑technical audiences.
• Proficient in Python, SQL, and modern ML tooling (TensorFlow, PyTorch, MLlib, feature stores) and experience with PySpark or equivalent distributed data processing tools.
• A strong academic background, typically a Master’s or PhD in a technical or quantitative field specialising in Machine Learning.
• Experience deploying models into production and maintaining business‑critical or consumer‑facing models.

Nice to Have:

• Background in gaming, e‑commerce, or subscription‑based businesses.
• Industry experience with experimentation frameworks and causal inference.
• Experience working in agile, fast‑paced environments.