Design and implement data models and machine learning algorithms to detect and prevent fraudulent behavior across IT systems, transactions, and user activities.
Analyze large-scale datasets from logs, identify systems, network security, and endpoint telemetry to identify anomalies and fraud patterns.
Develop and maintain fraud risk scoring mechanisms and automated alerting systems to translate fraud indicators into actionable intelligence and policies.
Create dashboards and visualizations to communicate fraud trends, incident metrics, and model performance to stakeholders and auditors.
Support regulatory compliance efforts (e.g., ISO 27001, NIST, GDPR) by providing evidence of control effectiveness and data-driven risk assessments.
Conduct post-incident reviews using forensic data to improve detection and prevention frameworks.
Requirements
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or a related field.
3+ years of experience in data science, fraud analytics, or cyber threat detection, ideally in a regulated or high-risk environment.
Proficiency in Python, R, or similar languages for statistical modeling and machine learning.
Experience with tools such as SQL, Spark, Scikit-learn, TensorFlow, or similar tools.
Familiarity with security data formats (e.g., syslogs, JSON, NetFlow) and SIEM platforms (e.g., Splunk, QRadar, ELK).
Solid understanding of cybersecurity principles, fraud vectors, and control frameworks (e.g., NIST CSF, ISO 27001, COBIT).
Strong knowledge of anomaly detection, supervised and unsupervised learning, and time-series analysis.
Ability to communicate technical findings to non-technical stakeholders effectively.
Strong critical thinking, problem-solving, and statistical analysis capabilities.
Understanding of threat modeling, risk tolerance, and mitigation strategies.
Ability to work cross-functionally with the GRC team, compliance, and engineering teams.
Able to present complex insights in a clear, concise manner tailored to different audiences.
