Senior Data Scientist - Anticheating

Role Summary

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

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

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

🧠 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

Responsibilities

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

Requirements

4+ years in Data Science / Machine Learning roles

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