Lead Fraud Data Scientist

Felix Technologies, Inc. • Miami, FL

7d • Hybrid

About The Position

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. 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.

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.

Requirements

• 5+ years of experience in a hands-on data science role, building and deploying machine learning models.
• Proven experience leading complex data science projects from inception to production, including setting technical direction and guiding peers.
• Expert-level Python for data analysis and modeling (pandas, scikit-learn, etc.).
• Advanced SQL skills for complex data extraction and manipulation.
• Deep experience with tree-based ML models (XGBoost, CatBoost, LightGBM) and statistical models (Logistic Regression, Lasso/Ridge).
• Deep understanding of model explainability frameworks (SHAP, LIME) and algorithmic fairness to ensure models comply with credit lending regulations.
• Strong understanding of sampling techniques for handling highly imbalanced datasets.
• Practical experience with clustering and outlier detection techniques (e.g., K-Means, K Nearest Neighbors, Isolation Forest).
• Proven experience with the full modeling lifecycle, including model deployment, monitoring, and maintenance on a cloud platform like GCP, AWS, or Azure.
• A solid foundation in statistics and experience designing and analyzing A/B tests.
• Excellent stakeholder management and communication skills, with a demonstrated ability to explain complex technical concepts to diverse audiences.
• Advanced English level.

Nice To Haves

• Direct experience in a FinTech, payments, or risk/fraud-focused role, particularly with exposure to credit or consumer lending.
• Experience working with traditional credit bureau data (Experian, Equifax, TransUnion) and alternative credit/identity data sources.
• Experience with Graph Neural Networks (GNNs) or graph analytics tools (e.g., Neo4j, NetworkX) to map complex fraud networks.
• Familiarity with consumer lending regulations (e.g., FCRA, ECOA) and their impact on machine learning model development.
• Hands-on MLOps experience (e.g., CI/CD for models, versioning, automated retraining).
• Experience with Google Cloud Platform (GCP), especially Vertex AI.
• Spanish and/or Portuguese speaker

Responsibilities

• Define the long-term machine learning strategy for the fraud team, establish technical best practices, and mentor junior data scientists.
• Own the entire lifecycle of fraud detection models, from data exploration and feature engineering to model training, validation, deployment, and monitoring.
• Design and develop models specifically targeted at lending fraud typologies, including synthetic identity fraud, first-party loan default fraud, and application fraud.
• 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.
• Design and execute A/B tests to measure the impact of new models, rules, and strategies on both fraud detection rates and user experience.
• 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.
• Deploy, monitor, and maintain machine learning models in a cloud environment, ensuring high availability and performance.
• 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.

Benefits

• Competitive salary
• Initial stock options grant
• Annual performance bonus
• Health, dental, and vision plans
• Remote work environment, although we have offices in Miami and México City and would love to work in hybrid model if you are up to it.
• Continuous learning opportunities
• Unlimited PTO
• Paid parental leave
• Empowering opportunities for growth in a dynamic entrepreneurial environment

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed