Lead Fraud Data Scientist

TL;DR

Lead Fraud Data Scientist (Python/Machine Learning): Building and deploying real-time fraud detection models for lending and cross-border payments with an accent on feature engineering, model explainability, experimentation, and production monitoring. Focus on uncovering synthetic identities, organized fraud rings, and complex user behavior through unsupervised learning, network analysis, and statistically rigorous model evaluation.

Location: Currently remote, with a transition to a hybrid, hub-centric setup across Mexico City, Guadalajara, Bogotá, Buenos Aires, Rio de Janeiro, and São Paulo.

Company

hirify.global is a hyper-growth fintech building an AI-powered WhatsApp-based remittance and financial ecosystem for Latin immigrants in the U.S., using AI, blockchain, and stablecoins.

What you will do

• Define the long-term machine learning strategy for fraud detection and establish technical best practices.
• Lead the full lifecycle of fraud models, from data exploration and feature engineering through deployment, monitoring, and maintenance.
• Develop models to detect lending fraud, including synthetic identity, first-party loan default, and application fraud.
• Investigate emerging fraud patterns and organized fraud rings using clustering, outlier detection, network analysis, and other unsupervised techniques.
• Design A/B tests and evaluate the effect of models, rules, fraud losses, false positives, and user experience.
• Collaborate with Product, Engineering, Risk, and Operations teams to integrate ML scores with rule engines and communicate results.

Requirements

• 5+ years of hands-on data science experience building and deploying machine learning models.
• Experience leading complex data science projects, setting technical direction, and mentoring peers.
• Expert Python and advanced SQL skills, with strong experience in pandas, scikit-learn, tree-based models, and statistical models.
• Experience with model explainability, algorithmic fairness, imbalanced-data sampling, clustering, and outlier detection.
• Experience deploying, monitoring, and maintaining models on GCP, AWS, or Azure, plus a strong foundation in statistics and A/B testing.
• Advanced English level required, along with strong stakeholder management and communication skills.

Nice to have

• Experience in fintech, payments, lending, risk, or fraud.
• Experience with credit bureau, alternative credit, or identity data.
• Graph analytics or Graph Neural Networks, including Neo4j or NetworkX.
• Consumer lending regulatory knowledge, MLOps, CI/CD, or Google Cloud Vertex AI experience.
• Spanish and/or Portuguese language skills.

Culture & Benefits

• Remote setup transitioning toward a hybrid, hub-centric collaboration model.
• Initial stock options grant and annual performance bonus.
• Health, dental, and vision plans.
• Unlimited PTO and paid parental leave.
• Continuous learning and growth opportunities in an entrepreneurial environment.