Senior Data Scientist, Square Financial Services, Credit Policy

<p>Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.</p> <p><strong>The Role</strong></p> <p>As a Data Scientist within the Credit Policy team of Square Financial Services (SFS), you will work across both commercial (Square) and consumer (Cash App) banking lending products. While core model development sits with our adjacent ML teams, the Credit Policy team provides crucial oversight and strategic direction for our underwriting processes. This includes monitoring existing products and their performance to maintain efficient underwriting, analyzing and shaping new product initiatives for growth, and ensuring regulatory compliance through documentation and process.</p> <p>You'll collaborate closely with a broad range of functions including ML, product, software engineering, operations, and capital markets teams to drive impact across our portfolio.</p> <p>Work from anywhere: This role can be performed from any location in the US with the flexibility to work from home</p> <p><strong>You Will</strong></p> <ul> <li>Analyze large datasets using SQL and Python to surface actionable insights that directly inform our product and credit strategy that drive business decisions</li> <li>Approach problems from first principles, using a variety of statistical and mathematical modeling techniques to research and understand customer behavior and loan performance</li> <li>Partner closely with ML engineers throughout the modeling lifecycle to develop, deploy, evaluate, and monitor models and credit policies</li> <li>Maintain and improve our systems for simulating the impacts of model/policy changes and the decision engine that implements those rules in production</li> <li>Develop KPIs and measurement systems focused on credit resiliency and product performance monitoring.</li> <li>Build, visualize and report on metrics that drive strategy and facilitate decision making for key business initiatives</li> <li>Effectively communicate your work with team leads and cross-functional stakeholders on a regular basis</li> </ul> <p><strong>You Have</strong></p> <ul> <li>Minimum of 8 years of related experience with a Bachelor's degree; or 6 years and a Master's degree; or a PhD with 3 years experience; or equivalent experience.</li> <li>A background in Statistics, Mathematics, Biostatistics, Economics or related quantitative field</li> <li>Advanced proficiency with SQL and Python for data analysis</li> <li>Advanced proficiency building product metrics in data visualization tools (e.g. Mode, Looker)</li> <li>Direct experience in credit, lending, risk management, or financial services.</li> <li>Ability to turn unstructured business problems into concrete analyses that yield actionable insights and recommendations</li> <li>Working understanding of core ML concepts, with a focus on usage in decision making.</li> </ul> <p> </p> <p>We're working to build a more inclusive economy where our customers have equal access to opportunity, and we strive to live by these same values in building our workplace. Block is an equal opportunity employer evaluating all employees and job applicants without regard to identity or any legally protected class. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.</p> <p>We believe in being fair, and are committed to an inclusive interview experience, i ... (truncated, view full listing at source)