Senior Credit Risk Analyst

Felix Technologies, Inc.•New York, NY

1d•Remote

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 Félix expands nationwide, we are scaling our Credit Risk function from the ground up to support our new Consumer Lending division. We are looking for a Senior Credit Risk Analyst who lives and breathes data, portfolio mathematics, and lending dynamics. You will own the quantitative frameworks behind our Send Now Pay Later (SNPL) product, directly impacting our bottom line by managing credit policies, loan pricing models, and portfolio health monitoring. If you want to move away from rigid, legacy corporate banking structures and deploy agile credit strategies in a hyper-growth tech startup, this seat is for you.

Requirements

4+ years of deep analytical experience in Credit Risk, explicitly managing unsecured consumer lending, credit cards, or digital lending products.
Expert level in SQL and statistical data modeling (Python/R, dbt, or similar data infrastructures).
Proven track record of evaluating and defining credit policies, understanding tradeoffs between growth volume and loss provisions.
Experience working alongside data scientists to evaluate and integrate predictive ML credit scorecards.
Ability to package complex portfolio metrics into clear, high-level executive summaries for the C-suite and investment partners.
Thrives in an environment of rapid experimentation, moving fast to adjust credit strategies based on instant user behavior trends.
Equivalent competencies in any of the above will also be considered.

Responsibilities

Act as the lead credit analytics owner for the SNPL team, serving as the source of truth for credit insights.
Own and deliver executive-level reporting regarding portfolio health, NPL (Non-Performing Loans) trends, and macroeconomic risk factors.
Own and continuously optimize valuation frameworks, vintage analysis, and Net Present Value (NPV) credit models.
Support, design, and build end-to-end credit analytics frameworks for SNPL and future digital credit products.
Direct and manage the Acquisition Credit Policy, optimizing approval rates while keeping default risk tightly within target bands.
Maintain constant surveillance on credit distribution and reporting across multiple demographic cohorts.
Build, test, and iterate on Customer Management Credit Policies (e.g., credit limit increases/decreases, re-engagement rules).
Partner with engineering to integrate new scoring models and champion-challenger testing mechanics directly into our production portfolio performance.
Research, validate, and integrate external alternative data sources (e.g., bureau data, behavioral analytics) to enhance predictive models.