Credit Risk Expert (Experto/a en Riesgo de Credito) – Remote

Clara
Latin America Posted 7 April 2026

Tech Stack

Job Description

Ready to accelerate your career? Clara is the fastest-growing company in Latin America. We've built the leading solution for companies to make and manage all their payments. We already help over 20,000 large and growing businesses operate with agility and financial clarity through locally issued corporate cards, bill pay, financing, and a powerful B2B platform built for scale. Clara is backed by some of the most successful investors in the world, including top regional VCs like monashees, Kaszek, and Canary, and leading global funds like Notable Capital, Coatue, DST Global Partners, ICONIQ Growth, General Catalyst, Citi Ventures, SV Angel, Citius, Endeavor Catalyst, and Goldman Sachs - in addition to dozens of angel investors and local family offices. We’re building the financial infrastructure that powers high-performing organizations across the region. We invite you to join us if you want to be part of a fast-paced environment that will accelerate your career and support you to do some of the best work of your life alongside a passionate and committed team distributed across the Americas. What you'll do We’re looking for a Credit Risk Expert to help shape how Clara manages and scales risk in Latin America. You’ll work closely with a high-performing team of risk specialists, data scientists, and business partners to build predictive credit models and enable high-quality, data-driven decision-making. This role is instrumental in aligning credit strategies with business goals. You’ll drive innovation across our risk frameworks, help design and monitor model performance, and ensure Clara’s credit operations remain robust, scalable, and compliant. Your work will directly impact business growth, resilience, and the health of our credit portfolio. Your key responsibilities Lead and support the development of regulatory and non-regulatory credit risk models (e.g., expected credit losses) Drive innovation in predictive analytics and modeling frameworks that enhance decision-making and operational efficiency Own the Risk MIS : monitor credit policy performance, model outcomes, and portfolio trends Collaborate across Operations , Finance , and Data teams to align risk strategies with broader business priorities Ensure model monitoring, backtesting, and recalibration routines are best-in-class Provide clear insights and recommendations to senior leadership on portfolio performance and risk exposure Continuously improve data and modeling processes for scalability, accuracy, and compliance Maintain rigorous standards of data quality, documentation, and model governance Who you are We’re looking for someone who meets the minimum requirements to be considered for the role. The preferred qualifications are a bonus, not a requirement. Must haves Academic background in Actuarial Science, Mathematics, Statistics, Computer Science, or similar Proven experience in credit risk management in Latin American markets Strong proficiency in SQL and data visualization tools such as Metabase Deep familiarity with predictive modeling, risk reporting, and model performance evaluation Experience building and maintaining credit risk methodologies Fluency in English and Portuguese Excellent cross-functional project management and communication skills Nice to haves Experience in Fintech , especially in fast-paced or early-stage environments Exposure to credit card and payments ecosystems Familiarity with Latin American regulatory frameworks for credit losses Experience supporting financial audits and compliance documentation Working knowledge of Slack , Notion , or similar collaboration tools Understanding of financial conciliation or operational finance processes Spanish proficiency or interest in learning Clara’s Hybrid Policy Claridians in a hybrid mode split their time between working from the office, talking to or visiting customers, or working from home. This hits a balance between bringing people ... (truncated, view full listing at source)
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