Staff Machine Learning Engineer, Credit Products (Square Financial Services)
Develops and deploys ML models for credit underwriting in regulated banking, owning full stack from data curation to production decisioning. Leads MLOps initiatives and improves policies using advanced techniques for underserved segments. Requires 8+ YOE in ML production and technical degree.
About the job
Responsibilities
- Apply a rigorous scientific mindset to underwriting new customer segments, evaluating alternative external data sources and deploying advanced architectures to enhance predictive accuracy.
- Lead complex ML Operations and Infrastructure initiatives, such as scaling data ingestion or enabling complex neural networks.
- Design and implement the full credit modeling stack, managing the entire lifecycle of credit decisioning and production integration.
- Use data science techniques to leverage new data sources, handling messy datasets for business decisions.
- Identify and execute improvements to credit policy using analytical approaches for customer and portfolio outcomes.
- Support team in model updates and troubleshoot production issues.
- Operate within regulated banking framework, balancing innovation with compliance.
Requirements
- Minimum 8 years experience with Bachelor's; 6 years with Master's; 3 years with PhD, focused on ML/statistical models in production.
- Degree in technical field (CS, Math, Stats, Physics, Engineering); preference for research or advanced degree.
- Strong quantitative intuition, data visualization, exploratory analysis.
- Full-stack proficiency across data pipelines to software architecture preferred.
- Clear communication with technical/non-technical audiences, especially executives.
- Pragmatic problem-solving balancing business, technical, regulatory constraints.
- Experience with tree-based models/gradient boosting helpful.
Compensation
Zone A: $228,700—$343,100 USD Zone B: $217,300—$325,900 USD Zone C: $205,900—$308,900 USD Zone D: $194,500—$291,700 USD
Skills
Machine Learning, Statistical Modeling, Ml Operations, Data Pipelines, Gradient Boosting, Tree-Based Models, Neural Networks, Python, Data Visualization, Production Deployment
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