Data Scientist – Fraud Detection & AML

About the role

We are looking for a Data Scientist to design, build and improve fraud detection, AML and financial crime models for Rasēd’s platform. The role covers the full analytics lifecycle from data exploration to model deployment and monitoring, working closely with fraud, compliance and engineering teams.

Key responsibilities

• Develop and deploy fraud detection, AML and financial crime prevention solutions.
• Build and refine models and rules that identify suspicious behavior, transaction fraud, mule activity, account takeover, sanctions risk and abnormal customer patterns.
• Translate fraud, compliance and business requirements into analytical solutions, detection scenarios, dashboards and workflows.
• Own end‑to‑end fraud analytics components, from data exploration and feature engineering to testing, deployment and monitoring.
• Work with structured financial, customer, device, transaction and case‑management data to uncover fraud patterns and risk indicators.
• Experiment with and optimise fraud detection models, risk scoring logic and scenario thresholds.
• Support model deployment and monitoring with engineering teams, ensuring models remain accurate, explainable and operationally useful.
• Help tune fraud scenarios to reduce false positives, improve detection rates and measure model effectiveness.
• Stay updated on fraud trends, AML typologies, regulatory requirements and industry best practices.

Required profile

• 1–4 years of experience delivering data science, analytics, fraud detection, AML or financial crime solutions.
• Hands‑on experience with Python.
• Strong exposure to fraud detection, AML, risk analytics or transaction monitoring.
• Proficiency in SQL and experience working with structured data such as transactions, customer profiles, device data, alerts or financial records.

Required skills

• Python
• SQL
• Feature engineering
• Data pipelines
• Model training, evaluation and deployment