- Fraud Performance Ownership: Own fraud performance for our payment instruments and rails. For each material fraud vector, diagnose the root mechanism and design the highest-leverage way to bring it down — biasing toward durable product and action-based controls over rule sprawl.
- Friction Optimization: Use the full toolkit beyond rules: step-up authentication (3DS / SCA), holds, paused payments, dynamic friction, and product/UX changes — applying the right amount of friction to the right customer at the right moment.
- Cross-Functional Partnership: Partner with Product and Engineering to embed fraud controls into the payment and money-movement experience, rather than bolting rules on top after the fact.
- Instrument & Rail Expertise: Bring deep, instrument-and-rail-specific expertise: card authorization and 3DS/SCA, ACH return codes and NACHA mechanics, settlement timing and the fraud windows it creates, tokenized wallets (Apple/Google Pay), and chargeback reason codes.
- Experimentation & Validation: Design and run experiments (A/B tests) to validate that a control actually reduces the target vector — and quantify its cost to conversion and good-customer experience.
- Deep-Dive Threat Analysis: Conduct deep-dive analyses on payment fraud schemes, organized fraud operations, and emerging threats across rails to uncover risky behaviors and turn them into action.
- Model Feature Engineering: Partner with Data Science to inform payment fraud models — providing domain expertise on vectors and feature engineering (model development not required).
- Stack Orchestration: Author and maintain decisioning logic in our orchestration stack (Sardine / Oscilar) where rules are genuinely the right tool — and retire rules that overlap or underperform.
- Education: Bachelor's degree in a quantitative or related field (Math, Economics, CS, Statistics, Data Science) — or equivalent practical experience.
- Fintech Experience: 4–6 years in fraud / payment risk, with demonstrated depth in payment-instrument and rail mechanics (this is the core of the role, not a nice-to-have).
- Authentication Tools: Strong working knowledge of 3DS / step-up authentication and how to deploy it to reduce fraud without over-penalizing good customers.
- Payment Surfaces Mastery: Deep knowledge of payment systems and their fraud surfaces: cards, ACH (return codes, NACHA), wires, settlement timing, tokenized wallets, and chargeback mechanics.
- Product Mindset: Proven ability to reduce fraud through product and action design, not only rule-writing. You think in terms of vectors and levers, not rule counts.
- Advanced Analytics Stack: Advanced SQL and Python, with the ability to work with large datasets and run complex analysis independently.
- Testing Rigor: Experience designing and reading experiments (A/B testing) to measure control effectiveness and conversion trade-offs.
- Machine Learning Concepts: Understanding of machine-learning concepts and their application to fraud detection (development skills not required).
- Tooling & Visualization: Experience with BI tools (Looker, Tableau, or similar).
- Core Competencies: Strong communication skills for both technical and non-technical audiences, advanced English, and a strong analytical curiosity to go deep into the data to find fraudsters.
- These are the applicable requisites, although equivalent competencies in any of the above will also be considered.
- 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
Skills Required
- Bachelor's degree in a quantitative field (Math, Economics, CS, Statistics, Data Science) or equivalent practical experience.
- 4-6 years in fraud/payment risk with deep payment-instrument and rail mechanics experience.
- Strong working knowledge of 3DS / step-up authentication and deployment to reduce fraud while minimizing customer friction.
- Deep knowledge of payment systems and fraud surfaces: cards, ACH (return codes, NACHA), wires, settlement timing, tokenized wallets, and chargeback mechanics.
- Proven ability to reduce fraud through product and action design (product mindset) rather than rule proliferation.
- Advanced SQL and Python with ability to work with large datasets and run complex analyses independently.
- Experience designing and interpreting experiments (A/B testing) to measure control effectiveness and conversion impact.
- Understanding of machine-learning concepts as applied to fraud detection (model development not required).
- Experience with BI/visualization tools (Looker, Tableau, or similar).
- Strong communication skills for technical and non-technical audiences, advanced English, and strong analytical curiosity.
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.







