Financial Crime Product - Data Scientists & Engineers

Financial Crime Product - Data Scientists & Engineers Alexander Barnes are partnered with a high-growth fintech building out its financial crime product capability across fraud, transaction monitoring, and screening.These roles sit within product and engineering, in the first line.You’ll either be defining how detection works, or building the systems it runs on.What you’ll be doingBuilding and improving detection systems across fraud and AML (card, banking, TM, screening)Working directly with transaction, auth, behavioural, and network data to identify patterns and signalsDeveloping detection logic across rules, models, and AITuning systems continuously. False positives, detection coverage, operational load, customer impactDesigning features and intelligence layers that improve how risk is detectedRunning deep analysis in SQL and Python. No reliance on dashboardsTranslating typologies into production-ready signals and decisioning logicDeploying and scaling models and rules in real-time systemsPartnering closely with product, engineering, and compliance to evolve detection frameworksWhere this role can sitDepending on your background, this leans into one of the following:Fraud Risk (Card / Banking)CNP, ATO, scams, APP, mule detection, onboarding abuseWorking with auth data, payment flows, chargebacks, behavioural signalsTransaction Monitoring (AML)Owning system performance. Rule effectiveness, typology coverage, backlog, false positivesWorking closely with ML models and monitoring frameworksScreening (Sanctions / PEP)Match quality, list coverage, tuning logic, global screening performanceDetection Engineering / ML SystemsBuilding monitoring frameworks from scratchDeploying models into productionScaling decisioning systems across large transaction volumesWhat we’re looking forHands-on experience in fraud, AML, or financial crime riskStrong understanding of typologies and how they translate into detection logicStrong SQL. Complex queries, large datasets, no hand-holdingPython for analysis and modelling (pandas, numpy; ML exposure expected)Experience building, tuning, or deploying detection systems (rules and/or models)Ability to think about systems end-to-end. Not just models, but performance and outcomesComfortable working across product, engineering, and complianceProfiles that tend to workFraud / risk data scientists from issuers, fintechs, or banksTM or screening specialists who understand system performance, not just policyEngineers who’ve built risk or monitoring systems at scaleInvestigators or law-side profiles who’ve moved into detection and pattern analysisWhat doesn’t workOps-only or case handling backgroundsCompliance or policy profiles without data or system ownershipEngineers with no exposure to financial crime or risk systemsPeople who can’t show what they’ve built, tuned, or improvedWhy this role existsMost teams measure financial crime after it happens.This team is building the systems that detect it earlier, adapt faster, and scale properly.