Lead Fraud Data Scientist

Félix is building the financial ecosystem for Latin immigrants in the U.S., beginning with a revolution in remittances. Our core product is an AI‑powered chatbot on Whats App that lets users send money home as easily as sending a text. Leveraging AI, blockchain, and stable coins, we make cross‑border payments faster, cheaper, and more accessible.

As Lead Data Scientist for our Fraud team, you will protect our company and customers by designing, building, and deploying real‑time fraud detection models. Your work will directly impact millions of dollars in fraud prevention and platform trust.

• Technical Leadership & Strategy:
  Define the long‑term ML strategy for the fraud team, establish best practices, and mentor junior scientists.
• End‑to‑End Model Development:
  Own the full model lifecycle—from data exploration and feature engineering to training, validation, deployment, and monitoring.
• Credit & Lending Fraud Mitigation:
  Build models targeting lending fraud typologies such as synthetic identity fraud, loan default fraud, and application fraud.
• Advanced Analysis:
  Investigate emerging fraud patterns and user behavior using clustering, outlier detection, network analysis, and other unsupervised techniques to uncover hidden risks and fraud rings.
• Experimentation:
  Design A/B tests to measure the impact of new models, rules, and strategies on fraud detection rates and user experience.
• Stakeholder
  
  Collaboration:
  
  Partner with Product, Engineering, Risk, and Operations to translate business needs into data solutions, integrate ML scores with rule engines, and communicate results to non‑technical audiences.
• Productionalize Models:
  Deploy, monitor, and maintain ML models in a cloud environment to ensure high availability and performance.
• Reporting & Visualization:
  Build dashboards in Tableau or Looker to track KPIs such as fraud loss rates, false positives, and model performance.

• 5+ years of hands‑on data science experience building and deploying ML models.
• Proven leadership of complex data science projects from inception to production.
• Expert Python (pandas, scikit‑learn, etc.) and advanced SQL skills.
• Deep experience with tree‑based models (XGBoost, Cat Boost, Light
  
  GBM) and statistical models (Logistic Regression, Lasso/Ridge).
• Strong understanding of model explainability (SHAP, LIME) and algorithmic fairness for credit lending compliance.
• Proficiency in sampling techniques for highly imbalanced datasets.
• Experience with clustering and outlier detection (K‑Means, K‑Nearest Neighbors, Isolation Forest).
• Full lifecycle and cloud deployment experience on GCP, AWS, or Azure.
• Solid foundation in statistics and A/B test design.
• Excellent stakeholder management and communication skills, with advanced English proficiency.

• Direct experience in Fin Tech, payments, or fraud/risk‑focused roles, especially in credit or consumer lending.
• Experience with credit bureau data (Experian, Equifax, Trans Union) or alternative credit/identity data sources.
• Knowledge of Graph Neural Networks or graph analytics tools (Neo4j, Network
  
  X) for mapping fraud networks.
• Familiarity with consumer lending regulations (FCRA, ECOA) and their impact on ML models.
• Hands‑on MLOps (CI/CD, model versioning, automated retraining).
• Experience with GCP Vertex AI.
• Spanish and/or Portuguese language skills.

• Competitive salary
• Initial stock option grant
• Annual performance bonus
• Health, dental, and vision plans
• Remote work environment with optional hybrid model at Miami or Mexico City offices
• Continuous learning opportunities
• Unlimited PTO
• Paid parental leave
• Opportunities for growth in a dynamic entrepreneurial environment

Félix is committed to providing equal employment opportunities to all qualified employees and applicants without regard to race, religion, nationality, sex, sexual orientation, gender identity, age, or disability. This policy applies to all terms and conditions of employment, including recruitment, hiring, placement, promotion, training, compensation, benefits, and termination.