Félix seeks to empower Latinos in the US to care for what matters most back home. #JuntosWeSucceed
The Role
Lead the fraud data science strategy and deliver end-to-end ML solutions to detect and prevent fraud in real time. Build, deploy, and monitor models (including lending fraud), run advanced unsupervised analyses, design A/B tests, collaborate with Product/Engineering/Risk, and produce dashboards to track KPI performance.
Summary Generated by Built In
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 cloud platform like GCP, AWS, or Azure.
- Analytical Rigor: A solid foundation in statistics and experience designing and analyzing A/B tests.
- Communication: Excellent stakeholder management and communication skills, with a demonstrated ability to explain complex technical concepts to diverse audiences. Advanced English level.
Nice to have
- Domain Experience: Direct experience in a FinTech, payments, or risk/fraud-focused role, particularly with exposure to credit or consumer lending.
- Alternative & Bureau Data: Experience working with traditional credit bureau data (Experian, Equifax, TransUnion) and alternative credit/identity data sources.
- Graph ML: Experience with Graph Neural Networks (GNNs) or graph analytics tools (e.g., Neo4j, NetworkX) to map complex fraud networks.
- Regulatory Familiarity: Familiarity with consumer lending regulations (e.g., FCRA, ECOA) and their impact on machine learning model development.
- MLOps: Hands-on MLOps experience (e.g., CI/CD for models, versioning, automated retraining).
- GCP / Vertex AI: Experience with Google Cloud Platform (GCP), especially Vertex AI.
- Spanish and/or Portuguese speaker
These are the applicable requisites, although equivalent competencies in any of the above will also be considered.
What We Offer
- Base
- SF/NYC: $168,000 - $214,000
- Miami: $137,000 - $177,000
- Initial stock options grant
- Annual performance bonus
- Health, dental, and vision plans
- Continuous learning opportunities
- 401(k) with an employer's match
- Unlimited PTO
- Paid parental leave
- Empowering opportunities for growth in a dynamic entrepreneurial environment
Equal Opportunity Employer
At Félix, we are 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.
Want to learn more about our privacy practices? Check out our Privacy Policy.
Skills Required
- 5+ years of hands-on data science experience building and deploying ML models
- Proven experience leading complex data science projects and mentoring peers
- Expert-level Python for data analysis and modeling (pandas, scikit-learn)
- Advanced SQL skills for complex data extraction and manipulation
- Deep experience with tree-based models (XGBoost, CatBoost, LightGBM) and statistical models (Logistic Regression, Lasso/Ridge)
- Experience with model explainability frameworks and algorithmic fairness (SHAP, LIME)
- Strong understanding of sampling techniques for highly imbalanced datasets
- Practical experience with unsupervised techniques (clustering, outlier detection, e.g., K-Means, KNN, Isolation Forest)
- Proven experience with full model lifecycle: deployment, monitoring, and maintenance on cloud platforms (GCP, AWS, or Azure)
- Solid foundation in statistics and experience designing and analyzing A/B tests
- Excellent stakeholder management and communication skills; advanced English
- Build and maintain dashboards using Tableau or Looker
- Direct experience in FinTech, payments, or fraud/risk (credit/lending) experience
- Experience with credit bureau and alternative credit/identity data (Experian, Equifax, TransUnion, alternative sources)
- Graph ML or graph analytics experience (GNNs, Neo4j, NetworkX)
- Familiarity with consumer lending regulations (e.g., FCRA, ECOA)
- MLOps experience (CI/CD for models, versioning, automated retraining)
- Experience with GCP Vertex AI
- Spanish and/or Portuguese speaker
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The Company
What We Do
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, the Félix founders were selected as “Endeavour Entrepreneurs” and were recipients 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.
