- The Manager, Credit Risk will lead and grow a team of Credit Risk Analysts, owning the day-to-day operations of the credit risk function while helping build it into something more ambitious: an AI-native credit risk practice, built for Jobber Payments today and ready to scale into a growing lending business

- This is a role for a builder, not a doer—someone who looks at a manual process or an ad hoc decision and asks how it should be automated, modeled, or systematized, rather than simply executed by hand

- You’ll work closely with Data Science and Risk Analytics to design and strengthen credit risk models, policies, and monitoring, and you’ll partner cross-functionally across Fintech, Product, and Customer Support to make sure credit risk decisions are embedded seamlessly into the products our customers use every day

- Lead, coach, and grow a team of Credit Risk Analysts, setting the standard for how the team investigates, decisions, and communicates credit risk

- Own credit risk operations end-to-end: underwriting and exposure decisions, portfolio monitoring, loss forecasting, and reporting on credit risk performance and trends

- Partner closely with Data Science and Risk Analytics to build, validate, and improve credit risk models and decisioning logic, including scorecards, statistical models, and machine learning approaches where they add value

- Champion an AI-native approach to credit risk management—using AI and automation to build novel solutions to novel problems, not just to speed up existing workflows

- Build and strengthen the credit risk function for Jobber’s payments business today, and lay the groundwork for credit risk management of a growing lending business

- Work cross-functionally with Fintech, Product,



and Customer Support teams to embed credit risk policy and controls into product and operational workflows

- Develop a credit risk provisioning process and reserving practices building from IFRS9 or equivalent GAAP standards, partnering with Finance and Accounting as needed

- Identify opportunities to automate manual credit risk processes and drive them from idea through to implementation

- Report on credit risk exposure, portfolio health, and emerging trends to Fintech and Risk leadership

- Support broader Fintech and Risk initiatives on credit-risk-relevant topics, including vendor evaluations and new product launches- Solid written and verbal communication skills, with the ability to translate technical or quantitative findings into decisions that product, operations, and leadership stakeholders can act on
- A deeply data-centric approach to decision-making, with comfort working directly in the data rather than relying solely on others to produce it for you
- The ability to work autonomously with strong time management skills. We are a mix of fully remote and hybrid distributed across the country. While you’ll be part of a team, you’ll need to be a self-starter who can find direction and guidance proactively

- Proficiency in SQL and Python (or a similar language), enough to independently pull, analyze, and interrogate data and to collaborate credibly with Data Science and Analytics
- A genuine passion for fintech,



and curiosity about how payments and lending products should manage risk differently than legacy financial institutions

- Familiarity with IFRS9 or equivalent GAAP standards (e.g., CECL) for credit loss provisioning, or a strong ability to learn and apply this quickly
- The ability to embrace ambiguity and change—we don’t know what we don’t know, so you should be comfortable coming in and improving the process rather than waiting for one to be handed to you
- A builder’s mindset: a track record of solving problems through automation and tooling, not just process, and genuine enthusiasm for using AI to build new approaches to credit risk rather than only applying existing ones

- Demonstrated experience in credit risk, in financial services, fintech, payments, or lending, including people leadership or mentorship of an analyst team

- Familiarity with credit scorecards, including scorecard development, calibration, or monitoring

- Experience building or overseeing credit risk models or policies for a lending product (installment loans, lines of credit, merchant cash advances, or similar)

- Experience with modern payments or lending infrastructure (e.g., Stripe, Adyen, Plaid, or similar)

- Experience working with or evaluating AI/ML tooling or vendors for risk decisioning

- Familiarity with Slack, Salesforce, Asana, Confluence, or other collaboration tools

- Working familiarity with statistical and machine learning approaches used in credit risk management (e.g., logistic regression, decision trees, gradient boosting) — you don’t need to be a data scientist, but you should understand what these approaches do, how they work at a conceptual level, and when it’s appropriate to apply them

📌 Manager of Credit Risk (Toronto)
🏢 Jobber
📍 Toronto