Lead Fraud Data Scientist

Remote, San Francisco, New York, Miami Full-Time SF/NYC: $168,000 - $214,000 Miami: $137,000 - $177,000 2026-06-30

About Felix Technologies, Inc.

Félix is dedicated to building the financial ecosystem for Latin immigrants in the U.S., beginning with transforming remittances. Our flagship product is an AI-powered chatbot on WhatsApp, enabling users to send money home with the simplicity of a text message. We leverage cutting-edge technologies like AI, blockchain, and stablecoins to ensure cross-border payments are faster, more affordable, and more accessible than ever before.

We are a rapidly growing Series B company, backed by over $100 million from prominent global investors. We were recognized as an “Endeavour Entrepreneur” and received the CrossTech Fintech Startups Award. Our team consists of highly talented and dedicated individuals united by the goal of empowering our customers. We embrace radical transparency, constructive feedback, and a strong bias for action to build lasting value and a product that truly improves our users' lives.

About the Position

Introduction

At Félix, we are building a financial ecosystem for Latin immigrants in the U.S., starting with revolutionizing remittances. Our core product is an AI-powered chatbot on WhatsApp, enabling easy money transfers. We utilize cutting-edge AI, blockchain, and stablecoins for faster, more affordable, and accessible cross-border payments.

We are a hyper-growth Series B company backed by over $100 million in funding from top-tier global investors. Joining Félix means being part of a dedicated team with a shared obsession: empowering our customers. We foster a culture of radical transparency, constructive feedback, and a bias for action to build sustainable value and a product that truly enhances our users' lives.

As a Lead Data Scientist for our Fraud team, you will protect our company and customers by designing, building, and deploying sophisticated models to detect and prevent fraudulent activity in real-time. This high-impact role directly contributes to protecting millions of dollars and ensuring a trustworthy platform.

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 targeting 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

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 Have

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.