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Urgent! Principal Data Scientist Job Opening In Singapore, Singapore – Now Hiring A5 Labs

Principal Data Scientist



Job description

About the job

A5 Labs offers best-in-breed AI-driven security solutions that ensure fair play and integrity across all strategy-based games, including online games and beyond.

Our proprietary neural networks and deep reinforcement learning models enable non-invasive, high-accuracy detection systems for competitive play.

By combining advanced automation detection, exploitative modeling, and AI-driven game security, we help online gaming operators maintain trust, fairness, and integrity at scale.

As part of our team, you will be at the forefront of AI security research and development, building cutting-edge solutions that adapt to evolving threats in poker, strategy games, and other competitive gaming environments.

We are seeking a Principal Architect, a hands-on technical manager who will oversee the end-to-end anti-cheat technical pipeline, ensuring scalable, real-time detection.

This role will focus on the development, deployment, implementation, and integration of AI models into production environments, working closely with leadership, product, data scientists, ML engineers, and application developers to ensure seamless operation and enforcement of security measures.

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.

Requirements:

-Technical Skills

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

  • 4+ 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.


Required Skill Profession

Other General



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