Data Scientist

A high-growth fintech is looking to bring on a Senior Data Scientist to build and ship production-grade scam intelligence that runs before payments clear. You'll turn multi-source signals (transaction context, counterparty intelligence, behavioural patterns, unstructured evidence) into reliable, explainable risk decisions - under real-world constraints like latency, uptime, and auditability. About the company: The company is building a payment intelligence layer for banks - running real-time "investigations" on payments to provide rich context on the counterparty and situation. The goal: intercept scams while ensuring genuine payments flow smoothly. They're early-stage, moving fast, and working on problems where correctness, security and reliability are non-negotiable., You're a hands-on ML/AI builder who's comfortable owning the full loop: data * modelling * deployment * monitoring * iteration. You care about practical decisioning (not just metrics), you're thoughtful about trade-offs (customer experience vs protection), and you're excited about building systems that are explainable and bank-grade. What you'll do

• Build and ship scam risk models and signals (typology classification, risk scoring, decision logic)
• Engineer features across heterogeneous data: transaction context, behavioural sequences, counterparty signals, network/graph patterns, and unstructured evidence
• Design calibrated outputs (scores + reason codes) that are actionable and explainable for banking workflows
• Own evaluation end-to-end: leakage avoidance, cost-sensitive metrics, thresholding, phased rollouts, and post-incident learning
• Productionise ML: packaging, deployment, monitoring, drift detection, and retraining strategies
• Collaborate closely with backend/product teams to integrate intelligence into real-time payment flows
• Work alongside agent/LLM workflows for evidence gathering and synthesis, while keeping the decision core predictable and auditable

Strong experience shipping applied ML into production (not just experimentation)

• Strong Python + ability to write maintainable, tested code

• Strong SQL + comfort working directly with messy, high-volume data

• Solid modelling judgement: calibration, leakage, bias, thresholding, cost trade-offs, monitoring/drift

• Experience building decisioning systems where reliability, latency, and explainability matter Nice-to-haves:

• Experience in fraud/scams, payments, risk, trust & safety, AML, or adjacent domains

• Familiarity with graph/network features and entity resolution style problems

• Experience with MLOps tooling (model registry/MLflow, feature stores, orchestration)

• Comfort with cloud-native/event-driven systems and working closely with platform/backend engineers

• Experience integrating unstructured signals (text/embeddings/RAG style pipelines) into decision systems