Senior Data Scientist (FinCrime / Fraud)

💻 **Job Title:** Senior Data Scientist **(** AI / ML Engineer) 💰 **Salary:** up to £135k (\+ very generous early\-stage equity, £100k\+) 📍 **Location:** Central London, EC1 (3 office day/week) 🏦 **Company:** B2B FinTech / Fraud Prevention 👥 **Employees** : \~25 💸 **Funding** : $15m\+ (Series A) This London startup is building a new intelligence layer designed to bring more context and security to digital payments. Their technology analyses transactions in real time, gathering signals from multiple sources to determine whether a payment is legitimate or potentially fraudulent. The platform combines distributed data systems, real\-time investigations and AI\-driven decisioning to help financial institutions detect scams while allowing legitimate payments to flow without unnecessary friction. Within 2 years of being founded, they're working with most Tier 1 banks and payment providers in the UK \- and are just getting started! They are now looking for an experienced **Data Scientist** ( **AI/ML Engineer)** **with deep fraud or financial crime experience** (ideally APP fraud exposure) to join at an early stage and help shape the core intelligence powering the platform. **Key responsibilities:** * Designing and deploying machine learning models used to detect fraud and financial crime in payment flows * Building features from heterogeneous data sources, including transaction data, contextual signals and unstructured information * Improving systems that extract useful signals from fragmented or unstructured data sources * Building reliable ML infrastructure to train, deploy and monitor models in production environments * Working closely with product and engineering teams to ensure models improve real\-world fraud outcomes * Identifying the fraud signals, typologies and data sources that meaningfully improve detection capability * Experimenting with both classical ML techniques and newer AI approaches where appropriate * Helping shape data strategy, including how feedback loops and labelling pipelines are built to improve models over time This role is focused on **shipping production systems rather than academic research** . **✅ Must have requirements:** * Strong practical experience building fraud detection systems or financial crime models in production * Deep FinCrime / FinTech / Payments domain expertise * Product mindset \- focus on improving real\-world outcomes, not just model metrics. * Experience working in fast\-moving environments where systems are built from scratch and priorities evolve quickly. * Experience working with heterogeneous datasets (transaction data, enrichment signals, text, network signals etc.) * Familiarity with model monitoring, drift detection and retraining pipelines * Strong SQL and data engineering capability * Strong programming skills in **Python** **👍 Bonus points for:** * Exposure to / understanding of APP Fraud, payment fraud or transaction monitoring * Previous experience working in an early stage start\-up and/or high growth scale up * Exposure to newer approaches such as LLM\-powered systems * Cloud infrastructure / data platforms experience, ideally GCP 🛂 **VISA** sponsorship is available if needed.