opengreenhousealter
Product Lead - Risk
Clara
LocationColombia
Last observed2026-06-24 08:29:35.644896
Job idalter-clara:greenhouse:5153328007
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 Product Manager, Risk to lead our new Risk Builder squad within the Biz Ops tribe. Risk is one of the highest-leverage areas in Clara’s product portfolio; getting credit lines, identity verification, and fraud signals right is what allows us to scale rapidly across LatAm without increasing losses. As the Product Manager for this lean, high-ownership squad, you will report to the Group Product Manager and partner directly with a senior engineer, Credit Risk, Fraud, Finance, and Data Science teams. You will own the full credit lifecycle from a product perspective—balancing high-velocity execution with strict risk mitigation. Your primary responsibilities will include: Owning the entire Risk product surface across both Acquisition and Portfolio Management, including onboarding risk checks, credit line increases/decreases, and internal tools for the Fraud Risk Operations team. Managing the integration layer with critical Credit and Fraud Risk vendors (Decisioning Engines, Credit Bureaus, SIFT, and ID Validation Vendors), mapping gaps, and making high-impact build vs. buy decisions. Translating complex risk model outputs into seamless product experiences by collaborating closely with Data Science and Credit Risk teams to design around model uncertainty. Defining and tracking core business metrics , including approval rates, fraud loss rates, credit utilization, credit line adjustment accuracy, and time-to-decision. Accelerating product delivery by writing clear PRDs, managing the product backlog, and driving cross-team alignment without relying on unnecessary meetings. Building no-code/low-code solutions and automating workflows using tools like N8N or Oscilar. Embracing a builder mindset by leveraging AI tools like Cursor, Claude Code, or v0 to prototype concepts, making straightforward code or configuration changes, and self-serving your own analytics dashboards. 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 Experience: 5+ years of experience in product management, with at least 2 years explicitly focused on risk, fraud, credit, identity, or lending-adjacent products within fintech or payment platforms. Technical Fluency: Ability to prototype concepts using AI coding tools, familiarity with GitHub (managing branches, commits, PRs, and reading diffs), and a solid conceptual understanding of API integrations and webhook-based event flows. Domain Knowledge: Deep understanding of credit-line mechanics (segmentation, adjustments), KYC/ID verification vendor stacks, and fraud signals (transaction blocking, step-up authentication). Data & Analytics Literacy: Strong analytical skills with the ability to write SQL queries, conduct cohort analysis, design experiments, and build independent d
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