Founding Machine Learning Engineer
Core
Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing) using proprietary reciprocal-bureau data and external signals to underwrite buyers and shape terms for vendors and buyers in the wholesale trade network.
Role type
Founding Machine Learning Engineer (Credit Risk & Capital Products)
Builds
A live, reciprocal trade-credit bureau (Lighthouse) enabling risk transfer and streamlined payments across the wholesale economy.
Domain
Fintech, wholesale trade, credit risk modeling, and financial networks.
Deliverable
production ML models
Required skills
supervised learning on tabular data, feature engineering, model selection, hyperparameter tuning, calibration, rigorous offline and online evaluation, end-to-end data pipeline ownership, Python, statistical reasoning (leakage, selection bias, label noise), production deployment and monitoring
Preferred skills
credit risk modeling (PD/LGD/EAD, scorecards, underwriting), fintech/lending/payments/fraud/insurance experience, ML platform components (feature stores, model registries, orchestration, serving, drift detection), backend engineering (APIs, distributed systems), LLMs and agentic systems, exploratory data analysis, founding ML hire experience
Technologies
Python, pandas, scikit-learn, PyTorch, TensorFlow, XGBoost, LightGBM, Jupyter, SQL
Responsibilities
Design and validate credit risk models against proprietary data; build ETL and feature pipelines; ship models into production systems with monitoring and drift detection; partner cross-functionally to translate business constraints into modeling decisions; help define hiring bars and interview processes for future data scientists and MLEs
Seniority
Senior, hands-on IC with founding/team-shaping responsibilities
