Responsibilities
- Partner with senior scientists to forecast cash flows and other drivers of business health using statistical and ML modeling.
- Design and build models that make our capital products better for customers and deliver positive return assets. Sharper pricing, lower losses, growth and offers that fit how SMBs actually run.
- Proactively mine our datasets for insight, and prototype new models that move the needle for customers and the business.
- Own model outcomes end-to-end. Not just deployment bespoke to the platforms Pipe serves (e.g., UberEats and Housecall Pro), but ongoing performance, residual analysis, drift detection, and the judgment calls about when a model needs to be retrained, replaced, or retired.
- Work closely with product, risk peers and engineering to make this real in production.
Requirements
- 3-5+ years building ML models, including training, evaluation, and deployment.
- Fluency in Python and the standard stack (NumPy, Pandas, scikit-learn, etc.).
- Working comfortably with agentic coding tools (Claude Code, Cursor, or similar) and standard dev tooling (GitHub, VS Code).
- Strong fundamentals in probability, statistics, and machine learning.
- Clear written and verbal communication; comfortable collaborating across functions and surfacing progress, tradeoffs, and results.
- Bachelor's in CS, Applied/Financial Math, Statistics, Economics, or a related technical field.
Nice to Have
- Master's is a plus, not a requirement.
- Prior experience in credit risk modeling, underwriting, or adjacent risk decision sciences (insurance pricing, actuarial, fraud) is a strong plus.
Work Arrangement
Remote (Worldwide)
Additional Information
- Fully remote company.
- Annual US base salary range for this role is $150,000 - $180,000.
