We're hiring a Machine Learning Engineer to build and own the models behind our underwriting and decisioning systems at FloatMe. Our models determine who gets approved, how much, and under what terms — serving customers across a wide range of profiles. 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. We move fast, test carefully, and hold our work to a high standard because the models we build determine real outcomes for real people. If you're excited to do rigorous, high-impact ML work at a fast-moving fintech, we'd love to hear from you.

What You’ll Do

You will be a senior individual contributor building and evolving the ML systems behind these products. You will work across the full modeling lifecycle: problem formulation, feature development, training, calibration, experimentation, deployment, monitoring, and iteration.
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.
Contribute analysis and perspective that inform portfolio-level decisions, including explaining model behavior, tradeoffs, and uncertainty to senior technical and business leaders.
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.

Technologies We Use and Teach:

Python (NumPy, Pandas, scikit-learn, PyTorch, XGBoost, LightGBM)
AI development tools as core infrastructure: Claude Code, Cursor, Copilot
ML flow for experiment tracking and model registry
Internal feature store and model hosting platform
SQL / Snowflake
GitHub
AWS
BI tools (Looker/PowerBI/Tableau)

Who You Are

A Master degree in a quantitative field (e.g., Mathematics, Statistics, Physics, Computer Science, Operation Research). A PhD degree is strongly welcomed.
5+ years applying AI, machine learning, or statistical modeling in decisioning contexts such as credit, risk, fraud, recommendations, or similar domains.
Experience with probabilistic models and decision systems, including calibration, score transformations, and interpretation of model outputs.
Strong experimentation skills: you know how to design holdouts, measure lift, and evaluate models beyond aggregate metrics.
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
Experience explaining modeling concepts, results, and limitations to senior stakeholders and cross-functional partners.

Bonus Points

Fintech background
Consumer finance experience (non-large bank environment)
Advanced modeling techniques
Background in small to medium sized companies

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