Data Scientist

• Personalisation & recommendations - Recommend relevant challenges, rewards, offers and in-game experiences using player behaviour, context and preferences; evolve from broad segments toward individual-level personalisation.
• Potential payer & value prediction - Identify players likely to make a first purchase, estimate future player value (LTV) and support thoughtful decisions about when and how to engage different player groups.
• Churn prediction & retention - Detect early signs of disengagement, understand why players leave and develop models that help target meaningful retention interventions.
• Player economy & monetisation - Find opportunities across virtual currency, game modes, in-app purchases and ads while balancing monetisation with healthy gameplay and player experience.
• Experimentation & incremental impact - Use A/B tests and causal approaches to evaluate whether model-driven interventions actually improve outcomes, not just predictions.

WHAT YOU WILL OWN:

• End-to-end applied ML - Frame product questions as measurable ML problems; develop features, train and evaluate models, and help operationalize scoring or recommendations.
• Rigorous measurement - Choose suitable offline and online metrics; address rare outcomes, data leakage, bias, calibration and model drift; design trustworthy experiments and guardrails.
• Cross-functional delivery - Partner closely with Product, Game Design, LiveOps, Analytics and Engineering to turn models into decisions, features and measurable results.
• Clear communication - Explain trade-offs, insights and recommendations to both technical and non-technical stakeholders; help shape the data science roadmap.

• Experience - 4+ years in data science or applied machine learning, with evidence of taking models beyond notebooks into real product or business decisions.
• Technical strength - Strong Python and SQL skills; practical experience with feature engineering, supervised learning, model evaluation and libraries such as scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow.
• Statistical judgment - Solid grounding in probability, statistics, experimentation and interpreting model performance for imbalanced classification, ranking or regression problems.
• Execution mindset - Comfort working with large behavioral/event datasets, ambiguous problems, iteration, version control and cloud environments such as AWS, Azure or GCP.
• Product curiosity - An ability to connect technical results to player behavior, user experience and measurable business impact., * Domain experience - Free-to-play/mobile gaming, consumer apps, recommendations, churn prevention, payer propensity, LTV forecasting or lifecycle marketing.
• Advanced methods - Recommender systems, uplift modeling, contextual bandits, reinforcement learning, causal inference, deep learning or production ML/MLOps.
• Emerging technologies - Interest in applying LLMs or agentic AI to meaningful analytics or player-experience problems.

Moonfrog was founded in late 2013 by a team of industry veterans with the mission of making mobile gaming inclusive in India.

With successful games like Ludo Club, Jalebi and Teen Patti Gold, Moonfrog has learned not only to breathe life into evergreen family games, but also continues to pioneer in the development of new mobile games, like Baahubali and Kalyug.

Our unique and powerful engineering and product capabilities ensure that subscribers to our games receive a delightful and superior gaming experience.

Moonfrog Labs is part of Stillfront. Stillfront is a global gaming company with 1400+ employees. We develop a wide range of digital games that attract over 50 million players each month. From well-established franchises like Supremacy, Big Farm, and BitLife to niche games, we span many different genres, including strategy, simulation, RPG and action, and casual and mash-up games. Data, Analytics & Data Platform · Bengaluru · Onsite