Architect, Machine Learning​/ ML Engineer

Apply statistical modeling and data science techniques to transaction and TCP L4 telemetry to identify anomalous traffic and behavioral patterns.

• Apply statistical modeling and data science techniques to transaction and TCP L4 telemetry to identify anomalous traffic and behavioral patterns.
• Build behavioral baselines and anomaly detection models using time-series analysis, clustering, correlation, outlier detection and statistical thresholds.
• Engineer features from transaction, connection, velocity, retry, batch and historical alert data to improve detection accuracy.
• Develop statistical scoring and classification models to differentiate legitimate, duplicate, retry, anomalous and potential DDoS activity.
• Analyze historical incidents and alerts to optimize detection thresholds and reduce false positives.
• Integrate statistical detection signals into agentic AI investigation workflows for automated correlation, investigation and evidence generation.
• Validate models using historical and simulated traffic scenarios and measure precision, recall, false-positive/false-negative rates and detection latency.
• Monitor model performance, behavioral drift and changes in traffic patterns and continuously tune detection algorithms.
• Partner with engineering and operations teams to product ionize models and provide explainable, statistically grounded recommendations to human investigators