Senior Data Scientist

Overview

Senior Data Scientist at Moonfrog Labs working on applied machine learning for player personalisation, monetisation, retention and experimentation.

What you'll do

• Recommend relevant challenges, rewards, offers and in-game experiences using player behaviour, context and preferences, evolving from broad segments toward individual-level personalisation.
• Identify players likely to make a first purchase, estimate future player value (LTV), and support decisions about when and how to engage different player groups.
• Detect early signs of disengagement, understand why players leave, and develop models that help target meaningful retention interventions.
• Find opportunities across virtual currency, game modes, in-app purchases and ads while balancing monetisation with healthy gameplay and player experience.
• Use A/B tests and causal approaches to evaluate whether model-driven interventions actually improve outcomes, not just predictions.
• Frame product questions as measurable ML problems; develop features, train and evaluate models, and help operationalize scoring or recommendations.
• Choose suitable offline and online metrics; address rare outcomes, data leakage, bias, calibration and model drift; design trustworthy experiments and guardrails.
• Partner closely with Product, Game Design, LiveOps, Analytics and Engineering to turn models into decisions, features and measurable results.
• Explain trade-offs, insights and recommendations to both technical and non-technical stakeholders; help shape the data science roadmap.

What you'll need

• 4+ years in data science or applied machine learning, with evidence of taking models beyond notebooks into real product or business decisions.
• Strong Python and SQL skills.
• Practical experience with feature engineering, supervised learning and model evaluation.
• Practical experience with libraries such as scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow.
• Solid grounding in probability, statistics and experimentation.
• Experience interpreting model performance for imbalanced classification, ranking or regression problems.
• Comfort working with large behavioral/event datasets, ambiguous problems, iteration and version control.
• Comfort working in cloud environments such as AWS, Azure or GCP.
• An ability to connect technical results to player behavior, user experience and measurable business impact.

Nice to have

• Domain experience in free-to-play/mobile gaming, consumer apps, recommendations, churn prevention, payer propensity, LTV forecasting or lifecycle marketing.
• Advanced methods such as recommender systems, uplift modeling, contextual bandits, reinforcement learning, causal inference, deep learning or production ML/MLOps.
• Interest in applying LLMs or agentic AI to meaningful analytics or player-experience problems.