Data Science Manager – Fraud Risk Engine
We are seeking a highly skilled and experienced Fraud Risk Engine Decision Scientist to lead the design, implementation, and optimisation of real-time fraud detection use cases using a fraud risk engine. This role bridges business understanding with technical expertise, enabling proactive fraud prevention through data-driven decisioning.
You will collaborate closely with business stakeholders to define fraud scenarios, identify and source relevant data, configure detection logic (rules, thresholds, models), and ensure accurate and timely decisions and alerts.
The ideal candidate will have extensive experience in fraud risk engine product implementation, fraud detection techniques, and a strong understanding of financial crime prevention strategies, including the use of cloud-based data services like AWS.
Key Responsibilities:
Partner with business teams to define and refine fraud detection use cases based on emerging threats, customer behaviour, and regulatory needs.
Identify, source, clean, and organise relevant data to support fraud detection. Perform exploratory data analysis to uncover patterns and insights.
Configure and tune rules, thresholds, and predictive models within the fraud risk engine to enable real-time decisioning.
Translate business needs into statistical or machine learning models. Validate and monitor model performance, ensuring alignment with governance standards.
Ensure fraud decisions are accurate, timely, and actionable. Design alerting mechanisms that balance fraud prevention with customer experience.
Present analytical findings and model outcomes to technical and non-technical stakeholders. Provide recommendations for continuous improvement.
Collaborate with cross-functional teams including architects, engineering, compliance, and business units to implement new fraud prevention initiatives.
Provide expertise during audits and compliance reviews related to fraud prevention controls.
Required Skills & Experience:
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field
- 5+ years in data science or analytics roles within the fraud and financial crime domain
- Proven experience in fraud, financial crime, compliance, or customer insight environments
- Extensive knowledge of fraud detection technologies, including rules-based, machine learning, and AI-driven solutions
- Strong programming experience in Python, R, Scala, or Java, along with experience in big data tools such as Apache Spark and Hadoop
- Proficiency in SQL, ideally SAS, and familiarity with tools like Jupyter, RStudio, Git, and Jenkins
- Solid understanding of machine learning techniques such as random forests, k-NN, and ensemble methods
- Experience with real-time decisioning systems and fraud risk engines
- Deep understanding of fraud schemes, payment processing, and financial crime prevention
- Experience with fraud prevention platforms and tools (e.g., FICO, SAS, Quantexa, Actimize, or similar)
- Hands-on experience with cloud services, especially AWS data stores (e.g., S3, Redshift, DynamoDB, RDS), including data ingestion, storage, and retrieval for fraud detection pipelines
- Familiarity with regulatory requirements regarding fraud prevention (e.g., PCI DSS, AML)
- Certification in fraud prevention platforms and tools
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