FloatMe
Machine Learning Engineer, Underwriting
San Antonio, TX
Sponsorship not specifiedDetected 1 day ago
PythonGitSQLSnowflakeAWSMachine LearningPyTorchscikit-learnPandasNumPyData VisualizationStatisticsA/B TestingResearchUnderwriting
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16Unrated
Cap-exempt (no lottery)0
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Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- The challenges are real: maintaining calibration across diverse customer populations, designing features that generalize as the portfolio grows, and balancing approval rates against loss performance at every decision point.
- As a senior individual contributor on our ML team, you'll work across the full modeling lifecycle - from problem formulation and feature development to deployment, monitoring, and iteration in production.
Responsibilities
- You will be a senior individual contributor building and evolving the ML systems behind these products.
- Build, evaluate, and maintain underwriting and decisioning models.
- Design and evolve underwriting decision frameworks, including the modeling, automation, policy logic and amount assignment that manage exposure over time.
- Design and run experiments to evaluate model performance, measure impact on approval rates and loss, margin and inform underwriting policy decisions.
- Develop deep understanding of consumer behavior, repayment dynamics, and portfolio structure, and use that to inform model design and decision logic.
- Develop and maintain the key portfolio KPIs and inventory of periodic analysis to continuously identify risk and growth opportunities
- Collaborate with Product, Engineering, Legal, Compliance, and Operations to ensure underwriting systems reflect business goals and regulatory expectations.
- We move fast, test carefully, and hold our work to a high standard because the models we build determine real outcomes for real people.
Requirements
- A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Operation Research).
- A PhD degree is strongly welcomed.
- Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs.
Skills
- Experience with model monitoring, degradation detection, and retraining strategies in production systems.
- Deep knowledge of underwriting using bank & cashflow analysis, bureau & alternative data etc. with a focus on unsecured credit risk
- Fintech background
- Consumer finance experience (non-large bank environment)
- Advanced modeling techniques
- Background in small to medium sized companies
- Bonus Points
- FloatMe is proud to be an equal opportunity employer.
Benefits
- We're hiring a Machine Learning Engineer to build and own the models behind our underwriting and decisioning systems at FloatMe.
- 5+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains.
Company info
- Our models determine who gets approved, how much, and under what terms - serving customers across a wide range of profiles.
- If you're excited to do rigorous, high-impact ML work at a fast-moving fintech, we'd love to hear from you.
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