Lead Fraud Data Scientist

About Us At Félix, we're building the financial ecosystem for Latin immigrants in the U.S., starting with a revolution in remittances. Our core product is an AI-powered chatbot built on WhatsApp, allowing our users to send money home as easily as sending a text message. We leverage cutting-edge technology like AI, blockchain, and stablecoins to make cross-border payments faster, more affordable, and more accessible than ever before. We are a hyper-growth Series B company, backed by over $100 million in funding from top-tier global investors, including QED, Castle Island, Switch Ventures, HTwenty, Monashees, and General Catalyst Customer Value Fund. This isn't just about the numbers; it's a testament to the trust our investors have in our vision and our team. Additionally, Félix was selected as an “Endeavour Entrepreneur” and was a recipient of the CrossTech Fintech Startups Award. We are a group of extremely talented and dedicated high-performers, united by our shared obsession with a single goal: empowering our customers. We are all owners of Félix, driven by a bias for action and a true experimentation spirit to get shit done with urgency and focus. Joining Félix means you will be part of a team building a legacy, a company that will outlive us all. This is a rare opportunity to apply your skills to a deeply meaningful mission—serving a community that has been underserved for too long. We are a team that is fiercely loyal to each other, where radical transparency and constructive feedback are how we grow and push for excellence. We are bold, we care less about what others are doing, and more about creating sustainable value and a product that truly makes our users' lives better. We are building the future, today. About

The Role

As a Lead Data Scientist for our Fraud team, you will be on the front lines of protecting our company and our customers. You will leverage your expertise in machine learning, statistics, and data analysis to design, build, and deploy sophisticated models that detect and prevent fraudulent activity in real-time. This is a high-impact role where you will see your work directly translate into protecting millions of dollars and ensuring a trustworthy platform for our users.

Responsibilities

Technical Leadership & Strategy: Define the long-term machine learning strategy for the fraud team, establish technical best practices, and mentor junior data scientists. End-to-End Model Development: Own the entire lifecycle of fraud detection models, from data exploration and feature engineering to model training, validation, deployment, and monitoring. Credit & Lending Fraud Mitigation: Design and develop models specifically targeted at lending fraud typologies, including synthetic identity fraud, first-party loan default fraud, and application fraud. Advanced Analysis: Conduct deep-dive investigations into emerging fraud patterns and user behavior, using clustering, outlier detection, network analysis, and other unsupervised techniques to uncover hidden risks and organized fraud rings. Experimentation: Design and execute A/B tests to measure the impact of new models, rules, and strategies on both fraud detection rates and user experience. Stakeholder Collaboration: Partner closely with Product, Engineering, Risk, and Operations teams to translate business needs into data science solutions, seamlessly integrate ML scores with rule engines, and communicate complex results to non-technical audiences. Productionalize Models: Deploy, monitor, and maintain machine learning models in a cloud environment, ensuring high availability and performance. Reporting & Visualization: Build and maintain dashboards using tools like Tableau or Looker to track key performance indicators (KPIs) like fraud loss rates, false positive rates, and model performance.

Requirements

Experience: 5+ years of experience in a hands-on data science role, building and deploying machine learning models. Leadership: Proven experience leading complex data science projects from inception to production, including setting technical direction and guiding peers. Python: Expert-level Python for data analysis and modeling (pandas, scikit-learn, etc.). SQL: Advanced SQL skills for complex data extraction and manipulation. Machine Learning Modeling: Deep experience with tree-based ML models (XGBoost, CatBoost, LightGBM) and statistical models (Logistic Regression, Lasso/Ridge). Model Explainability & Ethics: Deep understanding of model explainability frameworks (SHAP, LIME) and algorithmic fairness to ensure models comply with credit lending regulations. Sampling Techniques: Strong understanding of sampling techniques for handling highly imbalanced datasets. Unsupervised Learning: Practical experience with clustering and outlier detection techniques (e.g., K-Means, K Nearest Neighbors, Isolation Forest). Model Lifecycle & Cloud: Proven experience with the full modeling lifecycle, including model deployment, monitoring, and maintenance on a clo