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Jobber is a fast-growing fintech company serving small home service businesses (plumbers, painters, landscapers) with software for quoting, scheduling, invoicing, and payments. The Fintech department is a core strategic priority, and the Risk team is critical to enabling responsible expansion of Jobber Payments and a growing lending business.
You will lead and grow a team of Credit Risk Analysts, owning the end-to-end credit risk function while building it into an AI-native practice. This is a builder role—you'll look at manual processes and ad hoc decisions and ask how they should be automated, modeled, or systematized rather than executed by hand.
Key responsibilities:
- Lead, coach, and grow a Credit Risk Analyst team, setting standards for investigation, decision-making, and communication.
- Own credit risk operations end-to-end: underwriting, exposure decisions, portfolio monitoring, loss forecasting, and reporting on credit risk performance.
- Partner with Data Science and Risk Analytics to build, validate, and improve credit risk models, scorecards, statistical models, and machine learning approaches.
- Champion an AI-native approach to credit risk management, using AI and automation to build novel solutions rather than just speeding up existing workflows.
- Build and strengthen the credit risk function for Jobber Payments today and lay groundwork for a growing lending business.
- Work cross-functionally with Fintech, Product, and Customer Support to embed credit risk policy and controls into product and operational workflows.
- Develop credit risk provisioning and reserving practices aligned with IFRS9 or equivalent GAAP standards, partnering with Finance and Accounting.
- Identify and drive automation opportunities from idea through implementation.
- Report on credit risk exposure, portfolio health, and emerging trends to leadership.
- Support broader Fintech and Risk initiatives including vendor evaluations and new product launches.
Required qualifications:
- Demonstrated experience in credit risk within financial services, fintech, payments, or lending, including people leadership or mentorship of an analyst team.
- Builder's mindset with a track record of solving problems through automation and tooling; genuine enthusiasm for using AI to build new approaches to credit risk.
- Data-centric decision-making approach with comfort working directly in data.
- Proficiency in SQL and Python (or similar language) to independently pull, analyze, and interrogate data and collaborate with Data Science.
- Working familiarity with statistical and machine learning approaches in credit risk (logistic regression, decision trees, gradient boosting)—conceptual understanding and appropriate application.
- Familiarity with IFRS9 or equivalent GAAP standards (e.g., CECL) for credit loss.
