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