Staff Machine Learning Engineer, Underwriting and Credit
New
B
BlockFinancial Technology
This role is fully remote for candidates based in the US or Canada.Full-TimeStaff
Salary170,100 - 283,600 USD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- PythonSQLMachine LearningMLFlowPyTorchSnowflake
Requirements
- Bachelor's degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science).
- 10+ years applying AI, machine learning, or statistical modeling in decisioning contexts.
- Experience with probabilistic models and decision systems, including calibration and score transformations.
- Strong experimentation skills: experience designing holdouts and measuring lift.
- Experience with model monitoring, degradation detection, and retraining strategies in production.
- Proficiency with AI-native development workflows including LLMs and agentic coding tools.
- Experience explaining modeling concepts and results to senior stakeholders.
- Experience working across disciplines in environments with meaningful constraints.
Responsibilities
- Build, evaluate, and maintain underwriting and decisioning models across Cash App Borrow and Afterpay.
- Design and evolve credit decision frameworks, including the modeling, automation, and policy logic that manage credit exposure over time.
- Design and run experiments to evaluate model performance, measure impact on approval rates and loss, and inform credit policy decisions.
- Develop deep understanding of borrower behavior, repayment dynamics, and portfolio structure and use that to inform model design.
- Contribute analysis to inform portfolio-level decisions and explain model behavior to senior leaders.
- Work across the full modeling lifecycle: problem formulation, feature engineering, training, calibration, deployment, monitoring, and iteration.
- Build agentic engineering workflows that accelerate development, testing, and documentation.
- Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure credit systems reflect business goals.
- Shape how AI developer tooling is adopted across the team, defining review practices and quality standards.
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