Director, Merchant Risk Data Science
Paysafe
full-time
Posted on:
Location Type: Hybrid
Location: London • United Kingdom
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Job Level
About the role
- Design, prototype, and productionalize advanced AI/ML models for merchant fraud, abuse, chargebacks, and anomalous behaviour.
- Lead the adoption of modern modelling techniques, including: Transformers and sequence models Representation learning & embeddings Autoencoders and anomaly detection Graph / network-based models Semi-supervised and weakly supervised learning.
- Own the end-to-end model lifecycle: problem framing, feature representation, model development, validation, deployment, monitoring, and iteration.
- Partner closely with Risk Strategy, Operation, MLOps and Data Engineering to ensure models are scalable, explainable, and production-ready in real-time or near-real-time environments (e.g. AWS SageMaker).
- Act as a technical authority for advanced AI in merchant risk, influencing architecture, tooling, and long-term modelling strategy.
- Balance innovation with model governance, regulatory expectations, and risk controls, without limiting technical ambition.
- Partner with Data Engineering to shape: data requirements for advanced models feature stores and pipelines - the evolution of merchant risk data infrastructure.
- Act as a bridge between research, engineering, and business, ensuring models are both technically strong and operationally impactful.
Requirements
- 10+ years in data science / advanced analytics, with strong hands-on delivery experience.
- Demonstrated experience working with modern AI/ML techniques beyond tree-based models.
- Proven ability to take research concepts into production.
- Experience in payments, fraud, or risk is valuable but not mandatory.
- Expert-level Python and modern ML tooling.
- Strong hands-on experience with: Transformers, LSTM / sequence models Representation learning & embeddings Autoencoders & anomaly detection Graph / network modelling.
- Experience operationalising models on AWS SageMaker (or equivalent).
- Strong understanding of model evaluation, explainability, and monitoring in high-risk environments.
- Advanced degree (Master’s or PhD) in Statistics, Mathematics, Data Science, AI/ML, or a related field.
- PhD or equivalent advanced industry experience preferred.
Benefits
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Applicant Tracking System Keywords
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Hard Skills & Tools
AI/ML modelsTransformerssequence modelsrepresentation learningembeddingsautoencodersanomaly detectiongraph modellingsemi-supervised learningweakly supervised learning
Soft Skills
leadershipcollaborationproblem framingcommunicationinnovationmodel governanceinfluencetechnical authorityoperational impactcross-functional partnership
Certifications
Master’s degreePhDadvanced industry experience