Own the Fraud & AML Detection Architecture
Act as the technical lead for the fraud & AML rules engine, risk scoring logic, and behavioural monitoring pipeline
Design and improve the event flows, data schemas, triggers, and scoring components used for detection
Work closely with engineering to implement scalable and low-latency monitoring logic in production
Build and Optimise Rule Logic
Lead analysts in designing and refining technical detection rules (syntax, thresholds, conditions, event mapping)
Translate risk appetite into robust, efficient, and data-backed rules
Create testing frameworks, simulation tools, and regression checks for rules before deployment
Measure rule performance with clear metrics (latency, false positives, leakage, precision)
Integrate Risk Models & Behavioural Signals
Partner with data scientists to integrate ML-based risk scores, anomaly detectors, or velocity-based models into the rule engine
Define how risk signals are weighted, aggregated, or combined with deterministic rules
Ensure models and rules work coherently within the monitoring architecture
Engineering Collaboration & Technical Roadmap
Work closely with platform and backend engineers to improve system reliability and automation
Data & Event Quality Ownership
Define data requirements for fraud/AML systems: event mapping, attributes, enrichment
Lead investigations into data discrepancies and improvements to event quality
Partner with data engineering to ensure the pipeline is fit for detection logic
Leadership and Team Management
Manage a small team of technical analysts responsible for rules design and monitoring logic
Work with the Head of First Line Risk to prioritise work and align on roadmap
Build a strong engineering mindset in the fraud monitoring team: documentation, testing, performance measurement

Requirements

6+ years in fraud/risk engineering, data engineering for fraud, or technical fraud/risk analytics
Hands-on experience designing or maintaining fraud/AML rules engines, transaction monitoring systems, or risk scoring pipelines
Strong SQL skills and familiarity with distributed data systems (Snowflake, BigQuery, Redshift, or similar)
Understanding of event-driven architectures, stream processing, and real-time detection (Kafka, Pub/Sub, Flink, etc.)
Ability to work with engineering teams on API flows, backend logic, and alerting infrastructure
Deep understanding of fraud typologies (card fraud, account takeover, mule activity, merchant fraud, synthetic IDs)
Good understanding of AML detection logic (structuring, layering, suspicious patterns, velocity signals)
Experience interpreting model outputs and integrating risk signals into systems
Experience managing technical teams (analysts, engineers, or hybrid profiles)
Ability to convert complex detection problems into clear engineering requirements
Strong communication skills for working with engineers, data scientists, and risk leadership

Benefits

Flexible working hours
Health Insurance
Physical and mental health support through our partnership with MyFitness
25 days of Annual leave (+ Bank Holidays)
Possibility to visit other Teya offices to meet colleagues in instances when travel is safe and appropriate
Friday lunch in the office
Friendly, comfortable and high-end work equipment and informal office environment
Hybrid work mode policy

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

fraud detection architectureAML rules enginerisk scoring logicbehavioural monitoringSQLevent-driven architecturestream processingreal-time detectiondata requirements definitiontesting frameworks

Soft skills

leadershipteam managementcommunicationcollaborationproblem-solvinganalytical thinkingdocumentationperformance measurementprioritizationtechnical guidance