Senior Data Scientist – AI/ML for Autonomous Detection & Response (ADR SaaS Platform) Location: Remote (US-based preferred, Toronto, Brazil, Europe) Summary: We are hiring a Senior Data Scientist to lead AI and Machine Learning initiatives for the ASPIS Autonomous Detection & Response Platform. This role focuses on building real-time threat detection models, behavioral anomaly detection algorithms, and adaptive response engines integrating multi-source cybersecurity telemetry (endpoint, cloud, mobile, and network data). Key Responsibilities: ✅ AI Model Development • Lead the design and development of supervised and unsupervised ML models for: • Threat anomaly detection • Attack behavior correlation (MITRE ATT&CK-aligned) • Predictive risk scoring • Adaptive AI-driven response orchestration ✅ Data Pipeline Ownership • Build streaming data pipelines ingesting multi-tenant cybersecurity telemetry including: • EDR/XDR data • SIEM event streams • mobile threat detection signals • identity/access posture telemetry ✅ Autonomous Response Engineering • Collaborate on real-time inference pipelines enabling: • Sub-second threat classification • Confidence-based automated response triggers • Feedback loops for detection learning systems ✅ Cross-Platform ML Deployment • Deploy ML models in containerized microservices, optimizing for latency, scale, and multi-tenant inference separation. • Support both cloud-based AI inference (AWS/Azure) and on-premise/air-gapped deployments. ✅ Compliance-Aligned AI Practices • Implement explainable AI (XAI) techniques to ensure SOC analyst interpretability. • Maintain audit logs of AI decisions for FedRAMP, CMMC, SOC 2 alignment. Required Qualifications: • 10+ years in data science / machine learning, with at least 5+ years in cybersecurity AI applications. • Expert in Python ML ecosystems: scikit-learn, TensorFlow, PyTorch, and MLflow. • Strong experience in real-time inference, stream processing (Kafka, Redis Streams), and high-ingestion cybersecurity datasets. • Deep knowledge of behavioral anomaly detection, threat intelligence correlation, and adversary simulation data modeling. • Familiarity with SIEM/EDR/XDR telemetry formats, OCSF schema, and MITRE ATT&CK framework. • Practical understanding of compliance-aligned AI explainability, privacy-preserving ML, and secure ML lifecycle practices. Preferred Advantages: • Prior experience in autonomous cybersecurity response models, SOAR-triggered AI pipelines, or reinforcement learning for threat mitigation. • Experience optimizing AI inference in Kubernetes-based microservices. • Background in federated learning or privacy-enhancing ML for multi-tenant SaaS environments.