Senior Data Scientist - Anticheating
Summary
Build AI models to detect sophisticated cheating in competitive games by analyzing player behavior, using deep learning and adversarial techniques to distinguish skill from exploitation.
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