Staff Machine Learning Engineer, Credit Products (Square Financial Services)

Apply a rigorous scientific mindset to the challenge of underwriting new customer segments, involving the evaluation of alternative external data sources and the deployment of advanced architectures to enhance predictive accuracy. - Lead complex ML Operations and Infrastructure initiatives that advance our modeling capabilities, such as scaling data ingestion or enabling the use of more complex neural networks. - Design and implement the full credit modeling stack, taking responsibility for the entire lifecycle of credit decisioning and ensuring models are robustly integrated into production environments. - Use data science techniques to leverage new data sources for modeling, making sense of messy datasets and bringing clarity to business decisions. - Identify and execute material improvements to credit policy, applying an analytical lens to determine where technical or logic shifts can yield significant positive outcomes for the customer and the bank’s portfolio. - Support team members in ad-hoc and scheduled updates to existing models, and help troubleshoot issues in a real-time production environment. - Operate effectively within the framework of a regulated bank (SFS), balancing rapid innovation with the requirements of safety, soundness, and compliance. You Have

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, with a focus on developing and deploying machine learning and statistical models in production environments. - A degree in a technical field (e.g., Computer Science, Mathematics, Statistics, Physics, or Engineering). We have a strong preference for candidates with a demonstrated track record of scientific research or an advanced degree. - Strong quantitative intuition and data visualization skills, with a proven ability to conduct sophisticated ad-hoc and exploratory analysis. - Full-stack proficiency preferred, including the ability to contribute across the entire technical stack—from data pipelines to production-grade software architecture. - The versatility to communicate clearly with both technical and non-technical audiences, particularly in the context of high-visibility projects and executive stakeholders. - A pragmatic approach to problem-solving, with a willingness to utilize whichever tool is most appropriate for the situation while balancing complex business, technical, and regulatory constraints. - Experience with tree-based models and gradient boosting is helpful but not required; we value the ability to adapt and learn new methodologies as the credit landscape evolves. 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. We will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances. We believe in being fair, and are committed to an inclusive interview experience, including providing reasonable accommodations to disabled applicants throughout the recruitment process. We encourage applicants to share any needed accommodations with their recruiter, who will treat these requests as confidentially as possible. Want to learn more about what we're doing to build a workplace that is fair and square? Check out our I+D page. Block takes a market-based approach to pay, and pay may vary depending on your location. U.S. locations are categorized into one of four zones based on a cost of labor index for that geographic area. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future. To find a location’s zone designation, please refer to this resource. If a location of interest is not listed, please speak with a recruiter for additional information.