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Job details

Principal Data Scientist (Remote)

Key Responsibilities

Game Integrity & AI Research

  • Develop and deploy machine learning models to detect collusion, BOT / AI-assisted play, and other forms of cheating in online poker.

  • Leverage game theory, behavioral analytics, neural networks, , and deep reinforcement learning to identify unfair play patterns.

  • Design adversarial AI strategies to stress-test poker security models and proactively identify vulnerabilities.

  • Our current solution is based on a foundation neural network 

Automation & Bot Detection

  • Develop real-time bot detection models that analyze mouse movements, timing patterns, and decision consistency to differentiate human players from AI-assisted or fully automated bots.

  • Use keystroke dynamics, clickstream analysis, and behavioral biometrics to detect robotic play.

  • Research multi-accounting automation and ring-based bot networks, developing AI-driven countermeasures.

  • Implement graph-based network analysis to uncover bot farms and shared automation systems.

Game Theory & Exploitative Modeling

  • Research and implement game-theoretic AI models to analyze deviations from Nash equilibrium and identify potential cheating behaviors.

  • Develop exploitative modeling techniques to compare player behavior against optimal strategies and detect unnatural patterns.

  • Utilize inverse reinforcement learning to infer player intent and detect deviations from expected game dynamics.

  • Build multi-agent simulations to test different cheating scenarios and AI-driven countermeasures.

Technical Skills

  • PhD or Master’s in Computer Science, Machine Learning, Statistics, Mathematics, or a related field.

  • 7+ years of experience in neural networks, deep reinforcement learning , preferably in gaming, fraud detection, cybersecurity, or fintech.

  • Strong programming skills in Python, SQL, and distributed computing frameworks (Spark, Hadoop, or similar).

  • Experience with TensorFlow, PyTorch, or Scikit-learn for ML model development.

  • Hands-on experience deploying ML models in cloud environments (AWS, GCP, Azure) and optimizing for low-latency inference.

  • Strong foundation in game theory, Nash equilibrium, and multi-agent learning.

  • Familiarity with bot detection methods, anti-automation models, and behavioral fingerprinting.

  • Experience working with large-scale structured and unstructured data to detect patterns and anomalies.

  • Proficiency in MLOps, CI/CD for AI models, and real-time fraud detection pipelines.

Preferred Experience

  • Experience working with real-time fraud detection systems in gaming, cybersecurity, or financial technology.

  • Understanding of multi-accounting fraud, bot networks, and adversarial machine learning.

  • Experience with graph analytics, Bayesian inference, and behavioral clustering for adversarial behavior modeling.

  • Strong analytical and problem-solving skills, with a passion for ensuring fairness in online gaming.

Prior work with multi-agent reinforcement learning (MARL) systems or inverse reinforcement learning (IRL) is a plus.

Average salary estimate

$150000 / YEARLY (est.)
min
max
$120000K
$180000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

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EMPLOYMENT TYPE
Full-time, remote
DATE POSTED
April 5, 2025

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