Senior Data Scientist - Anticheating

About this role

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