<h3><strong>Who we are</strong></h3> <p><strong>About Stripe</strong></p> <p>Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.</p> <h3><strong>About the team</strong></h3> <p>The Portfolio and Policy Management team is responsible for setting the Stripe payments credit risk policy and monitoring performance across our global accounts with a specific focus on North American Users. We are responsible for identifying key trends in portfolio performance, managing risk for our largest users while ensuring the best user experience on the platform. The team is composed of curious, driven, and analytical individuals who are passionate about using their skills to shape the future of Stripe. We partner closely with Stripes engineering, data science, product, and servicing teams to leverage existing platforms, integrate industry best practices, and develop novel solutions to evaluate and manage credit risk. If you are interested in joining a fast-growing organization and applying your experience to shape the future of Stripe, we encourage you to apply.</p> <h2><strong>What you’ll do</strong></h2> <p>As a key member of the team, you will have the opportunity to shape the future of Stripes credit policy by driving meaningful changes to the risk framework. You will leverage Stripes vast data assets to formulate your recommendations and work closely with our partners. We are a small and lean team, which means you will have the autonomy and the responsibility to manage end-to-end risk initiatives.</p> <h3>Responsibilities</h3> <ul> <li>Leverage analytical and technical skills to provide credit risk recommendations, deliver insights, and support strategic business decisions. </li> <li>Analyze account activity and monitor portfolio trends to identify opportunities for Stripe to reduce potential credit losses. </li> <li>Collaborate with our technical teams - Risk Foundations, Engineering and Modeling (Data Science, ML’s).</li> <li>Understand our current credit controls and strategies to identify opportunities for improvement</li> <li>Deeply understand our performance metrics including loss trends and leading indicators</li> <li>Work closely with our core risk team to mitigate potential risks with specific users</li> <li>Lead cross-functional projects, manage timelines and deliverables, and effectively coordinate with various stakeholders to ensure alignment and successful implementation of credit risk initiatives.</li> </ul> <h2><strong>Who you are</strong></h2> <p>We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.</p> <h3><strong>Minimum requirements</strong></h3> <ul> <li>5+ years of experience in credit analysis and underwriting</li> <li>Bachelors degree in finance, economics, statistics, or a related field</li> <li>Experience in small business or credit risk management and/or financial lending </li> <li>Strong analytical skills and a rigorous, data-driven approach to problem-solving</li> <li>Experience working cross-functionally across multiple teams and time zones</li> <li>Strong stakeholder management and prioritization skills  </li> <li>Ability to thrive in an unstructured and fast-moving organization</li> <li>Strong technical expertise, including SQL, Excel and Google sheets</li> <li>Ability to quickly learn complex concepts and internal tooling</li> <li>Direct experience with modeling concepts across credit risk </li> <li>Self-starter who can work independently and can proactively identify gaps, create solutions and drive adoption and scale </li> </ul> <h3><strong>Preferred qualifications</strong></h3> <ul> <li>Experience with quantitative tools such as Python, R, or Stata</li> <li>Masters degree in finance, economics, statistics, or a related field</li> <li>Superior communication and relationship management skills</li> <li>Experience working with machine learning teams</li> <li>Ability to communicate results clearly with a focus on driving impact</li> </ul>