Senior Data Scientist in London

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Senior Data Scientist
In short:
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.
Who we’re looking for
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
Must-haves:
- 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-/event-driven systems and working closely with platform/backend engineers
- Experience integrating unstructured signals (text/embeddings/RAG style pipelines) into decision systems
Why join
- Work on a mission with real-world impact: stopping scams before money leaves
- Build real-time, bank-grade ML systems with ownership end-to-end
- Early team + high autonomy + meaningful technical decisions
- London hybrid working + visa sponsorship available
If you are interested in applying for this job please press the Apply Button and follow the application process. Energy Jobline wishes you the very best of luck in your next career move.
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Location:
London
Job Type:
FullTime
Category:
Data, Senior, Scientist, Science

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