Design and implement machine learning models for authentication, fraud detection, and cybersecurity threat prediction
Develop advanced behavioral analytics solutions to detect anomalies in user authentication and access patterns
Collaborate with cybersecurity teams to integrate AI-driven solutions into security platforms and workflows
Research and implement cutting-edge techniques in AuthAI, including deep learning for identity verification and continuous authentication
Build and maintain scalable data pipelines for security event monitoring and threat detection
Conduct data experiments to assess new authentication methods, biometrics, and risk-based models
Interpret and present insights to senior stakeholders to support data-driven decision-making in cybersecurity strategies
Ensure all models, methods, and solutions comply with data privacy regulations and organizational security standards

Requirements

Strong proficiency in machine learning, deep learning, and data science tools (Python, R, TensorFlow, PyTorch, Scikit-learn)
Expertise in AuthAI technologies such as biometrics, continuous authentication, and identity risk scoring
Solid understanding of cybersecurity principles, including intrusion detection, anomaly detection, encryption, and access management
Experience with threat intelligence platforms, SIEM tools, and security data analysis
Knowledge of cloud platforms (AWS, Azure, or GCP) with security and data services
Familiarity with data engineering workflows, including ETL, big data (Spark, Hadoop), and cybersecurity log analysis
Strong problem-solving ability with a focus on risk mitigation and predictive analytics
Excellent communication skills for collaboration with both technical and non-technical stakeholders

Digitals AI

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