A5 Labs is redefining the boundaries of AI-driven security in competitive online environments. We specialize in ensuring fair play, integrity, and trust in high-stakes, strategy-based games such as online poker and real-time competitive gaming platforms.

We're seeking an experienced data scientist with deep domain expertise in online gaming—especially poker—and game-theoretic modeling. You will be working on advanced detection systems that identify bots, collusion, and other unfair play tactics at scale using deep learning, reinforcement learning, and high-precision data pipelines.

Must-Have Domain Expertise (Not Optional)

Candidates must have significant prior experience in the online gaming or poker domain (ideally 5+ years), and a solid grasp of game-theoretic modeling as it applies to online competitive environments. Specifically:

Deep understanding of online poker ecosystems, gameplay mechanics, and fraud vectors such as collusion, multi-accounting, and botting.
Demonstrated experience with game theory concepts including:
Equilibrium strategies
Range construction
Solver outputs (e.g., PioSolver, GTO+)
Expected value (EV) / equity analysis

Applications lacking this domain-specific background will not be considered.

Responsibilities

Feature Engineering & Detection Systems

Design and scale ETL pipelines that process multi-modal gameplay, device, and network data across billions of hands or events.
Partner with RL researchers and data scientists to engineer high-value features for advanced anti-cheat detection.
Perform exploratory data analysis (EDA) to extract gameplay signals indicative of fraudulent or non-human behavior.

Model Monitoring & Operations

Build robust systems to monitor model health (precision, recall, drift, false positives).
Investigate anomalies and detection spikes with root-cause analysis and adversarial testing.
Collaborate on labeling strategies, performance tuning, and model iteration.

Cross-Functional Collaboration

Bridge gameplay knowledge with AI research by aligning models with real-world gaming
behavior and operator constraints.
Deliver detailed reporting and dashboards for executive and operational decision-making.
Work directly with gaming platform engineers to integrate detection models into production environments.

Requirements

Core Skills

Applications lacking this domain-specific background will not be considered.

5+ years of hands-on experience in data science, preferably with a focus on game analytics, adversarial modeling, or fraud detection.
Proficient in Python (Pandas, NumPy, PySpark) and SQL at scale.
Experience with distributed data processing tools (e.g., Spark, Kafka, Airflow).
Familiarity with model evaluation metrics (AUC, F1, precision/recall) and real-time model monitoring.

AI/ML Collaboration

Understanding of reinforcement learning (RL) and its applications to imperfect-information games.
Experience translating RL/model outputs (e.g., solver predictions, policy models) into production-grade features.
Ability to work closely with AI researchers and engineers in a fast-paced, iterative R&D environment.

Infrastructure & Visualization

Strong knowledge of cloud data warehouses (e.g., BigQuery, Snowflake), object storage (S3/GCS), and visual analytics platforms (e.g., Superset, Grafana).
Experience in building reproducible, scalable workflows for analytics and model deployment.

About the Team: AceGuardian

This role is embedded within AceGuardian, A5 Labs’ elite team dedicated to advanced game AI and security. We develop reinforcement learning agents, real-time detection systems, and behavioral analytics tools to tackle cheating, botting, and collusion in poker and other high-skill games.

What We Offer

Competitive compensation (4.5+ Glassdoor rating; 100% pay satisfaction)
Fully remote with flexible hours and generous paid leave (4–5 weeks extra)
Direct collaboration with world-class AI researchers and engineers
Product-focused culture — we ship real systems, not just white papers
Multicultural and inclusive team — fluent English not required, only strong collaboration skills

How to Apply

To be considered, your application must clearly demonstrate: