Data Scientist

-

Job Description:

• 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.

Requirement:

• 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.