Most Loved Workplace® Certified JobCredit Risk Strategy and Analytics
Assessed by Most Loved Workplace®About the Role
At Stripe, a Most Loved Workplace® certified employer in the Financial Services space, Economic infrastructure for the internet, powering ambitious businesses globally..
<p><span style="font-size: 18pt;"><strong>Who we are&nbsp;</strong></span></p>
<p><span style="font-size: 12pt;"><strong>About Stripe</strong></span></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 Capital team is responsible for managing the end-to-end risk strategy for Stripe's lending product. The team is based in the US and Canada, and 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 Stripe's 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 Capital team, you will have the opportunity to shape the future of Stripe's credit policy by driving meaningful changes to the risk and underwriting framework. You will leverage Stripe's vast data assets to formulate your recommendations and work closely with our partners and, where applicable, leverage third-party data. 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>Architect and implement credit policies based on Stripe's proprietary data and selected industry data to target, price, and size capital products.</li>
<li>Utilize your analytical and technical skills to provide credit risk recommendations, deliver insights, and support strategic business decisions.</li>
<li>Collaborate with cross-functional teams, such as Product, Data Science, Engineering, and Capital Markets to shape new product development across different geographies and industries.</li>
<li>Incorporate third-party data, including bureau, bank account, financial statements, and less traditional data, into our credit assessment.</li>
<li>Analyze account activity and monitor portfolio trends to identify opportunities for Stripe to reduce potential credit losses.</li>
<li>Help scale risk processes by working with partners to design and optimize outsourced workflows.</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>Bachelor's degree in finance, economics, statistics, or a related field.</li>
<li>Experience in risk management, financial lending, or core credit risk management function.</li>
<li>Strong analytical skills and a rigorous, data-driven approach to problem-solving.</li>
<li>Experience working with both internal and external stakeholders and the ability to closely. manage expectations and deliverables in a timely manner.</li>
<li>Ability to thrive in an unstructured and fast-moving organization.</li>
<li>Strong technical expertise, including SQL, excel, google sheets.</li>
<li>Self-starter who can work independently.</li>
</ul>
<h3><strong>Preferred qualifications</strong></h3>
<ul>
<li>Experience with quantitative tools such as Python, R, or Stata.</li>
<li>Master's degree in finance, economics, statistics, or a related field.</li>
<li>Superior communication and relationship management skills.</li>
<li>Experience with credit scoring models.</li>
<li>Experience working with machine learning teams.</li>
<li>Ability to communicate results clearly with a focus on driving impact.</li>
</ul>
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