Senior Data Scientist - Anticheating

About the Role We are building a next-generation AI-driven anti-cheating system for competitive strategy games. Unlike traditional fraud detection, our challenge sits at the intersection of: - 🎮 Game AI & player behavior modeling - 🧠 Reinforcement learning & decision systems - 🔍 Anomaly detection under adversarial conditions You will work on identifying non-obvious, strategic cheating behaviors in complex environments where players actively adapt to detection systems. This is not rule-based detection — this is behavioral intelligence at scale. What You’ll Do 1️⃣ Behavioral Modeling & Detection - Design machine learning / deep learning models to detect cheating patterns - Model player behavior sequences, strategies, and anomalies - Build systems that distinguish: - high-skill play vs. AI-assisted play - natural variance vs. exploitation 2️⃣ Anti-Cheating System Design - Develop scalable detection pipelines (offline + real-time) - Build feature systems from gameplay logs / event streams - Design evaluation frameworks for detection accuracy & robustness 3️⃣ ML / DL / Advanced Techniques - Apply and experiment with: - sequence modeling (RNN / Transformer-based) - anomaly detection - graph-based or behavioral embeddings - Explore intersections with: - reinforcement learning - game-theoretic modeling - adversarial ML 4️⃣ Collaboration with AI & Engineering Teams - Work closely with: - Gameplay AI / RL researchers - Backend / data engineering teams - Translate models into production systems What We’re Looking For ✅ Core Requirements - 4+ years in Data Science / Machine Learning roles - Strong foundation in: - deep learning - statistical modeling - Experience in one or more of: - fraud detection / AML - risk modeling - anomaly detection - behavioral analytics ✅ Strong Signals (Big Plus) - Experience with: - sequence models (LSTM / Transformer) - large-scale behavioral data - real-time detection systems - Exposure to: - reinforcement learning - game AI - adversarial systems ✅ Technical Stack - Python (must) - PyTorch / TensorFlow - SQL / data pipelines - Experience working with large-scale datasets Why This Role is Interesting - 🚀 Work on problems similar to fraud detection at scale — but harder - 🎯 Direct impact on real-money / competitive environments - 🧠 Blend of: - ML research - production systems - game AI - 🌍 Fully remote, globally distributed team Location & Visa - 🌏 Remote-first (global team) - 🇯🇵 Japan relocation supported (visa sponsorship available for qualified candidates) Who This Role is Perfect For - Data scientists bored with “dashboard ML” - Fraud / AML experts who want more complex, adversarial systems - ML engineers who want to work closer to decision intelligence & behavior modeling Originally posted on Himalayas