Principal Data Scientist

A5 Labs; United States; Remote; USD 150,000 - 200,000; Full time

Job summary

Join a forward-thinking company at the forefront of gaming integrity and AI research. This exciting role involves developing advanced machine learning models to combat cheating in online poker, leveraging game theory and behavioral analytics. You will design adversarial AI strategies to enhance security and uncover vulnerabilities, ensuring a fair gaming experience. Collaborate with a talented team to create real-time bot detection systems and implement innovative solutions that redefine standards in gaming integrity. If you are passionate about AI and fairness in gaming, this is the opportunity for you.

Qualifications

• 7+ years of experience in neural networks and deep reinforcement learning.
• Strong programming skills in Python and SQL with cloud deployment experience.

Responsibilities

• Develop ML models to detect cheating in online poker using game theory and behavioral analytics.
• Implement real-time bot detection models analyzing player behavior and patterns.

Skills

Neural Networks

Deep Reinforcement Learning

Python

SQL

Game Theory

Behavioral Analytics

MLOps

Graph Analytics

Education

PhD or Masters in Computer Science

Masters in Statistics

Tools

TensorFlow

PyTorch

Scikit-learn

AWS

GCP

Azure

Job description

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 masters 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.