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

✅ 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