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

Block•Bay Area, CA, United States of America, CA

35d•Remote

About The Position

The Credit and Lending team is responsible for the predictive intelligence that underpins Block’s primary capital-intensive products. These products unlock unique access to credit for our customers, many of whom are otherwise underbanked and underserved by the traditional financial system. As a Machine Learning Engineer within Square Financial Services (SFS), you will occupy a high-leverage role at the intersection of regulated banking and advanced autonomous systems. This position requires full-stack ownership of the credit engine, from the curation of novel data signals to the implementation of the decisioning logic that drives Block’s top-line growth. Our credit products are material drivers of the company’s profitability and are frequently highlighted in executive reviews and quarterly earnings reports. We are seeking a scientifically-minded contributor capable of delivering extraordinary individual leverage to expand our underwriting capabilities into previously untapped segments through pragmatic policy evolution and advanced modeling techniques.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.