Senior Data Scientist - Fraud Model Validation

Specialista Senior / Project Manager
You will perform end-to-end validation of fraud detection ML models, covering data, features, models, deployment, and monitoring. You’ll develop challenger approaches and scrutinize methodologies used by first-line teams. You will build agentic AI tools to automate validation workflows and surface risks. Your work directly supports governance, regulatory expectations, and responsible deployment in a fast-paced payments environment.
Validate end-to-end fraud ML models, including data integrity, features, deployment design, and monitoring
Develop challenger models and critique first-line methodologies and implementations
Build and deploy agentic AI tools to automate validation workflows and surface risks
Assess model performance using fraud-specific metrics and business impact trade-offs
Evaluate data representativeness, leakage risks, bias, and large-scale feature pipelines
Review model governance, explainability, privacy, and regulatory compliance
Assess CI/CD controls, deployment processes, and cloud environments
Develop and maintain validation frameworks and monitoring tools
Collaborate with data scientists, ML engineers, product, and business stakeholders
Document validation outcomes in line with governance standards and regulations
Stay updated on fraud typologies, ML/AI techniques, and regulatory developments
Advanced degree in a quantitative field (Master’s or PhD)
3+ years of hands-on fraud modeling experience
Deep ML lifecycle expertise from design to production monitoring
Strong Python and SQL;
PySpark/Spark

Experience with agentic AI workflows
Familiarity with cloud ML platforms (AWS Sage Maker, Lambda, S3, Athena) and deployment
Knowledge of model validation, governance, and regulatory expectations
Experience assessing bias, fairness, and privacy risks
Strong communication and ability to explain risks to senior stakeholders
Ability to work independently while constructively challenging teams
You will perform end-to-end validation of fraud detection ML models, covering data, features, models, deployment, and monitoring. You’ll dev…
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