Machine Learning Engineer

About the Company

Wholesail is building a financial network from the ground up that connects the systems of vendors and buyers involved in wholesale trade to enable streamlined payment and the transfer of risk to third parties. Through Lighthouse, they are building a live, reciprocal trade-credit bureau to underwrite risk using proprietary data.

Responsibilities

• Design, build, and validate credit risk models (PD, LGD, EAD, fraud, exposure sizing, pricing)
• Build data pipelines, feature logic, and training datasets
• Own model deployment, monitoring, drift detection, and iteration
• Partner cross-functionally with product, engineering, and capital markets teams
• Help define the hiring bar and interview process for future data science and MLE hires

Requirements

• 5+ years of experience building models for production use cases
• Strong command of supervised learning on tabular data
• Proficiency in Python and the modern ML ecosystem (pandas, scikit-learn, PyTorch or TensorFlow, XGBoost/LightGBM)
• Strong data engineering skills including ETL and feature pipelines using SQL
• Solid statistical reasoning regarding leakage, selection bias, and label noise
• Proven track record of taking models from problem statement to production systems
• Excellent English communication skills for explaining model behavior to non-ML audiences

Preferred Qualifications

• Direct experience in credit risk modeling (PD/LGD/EAD, scorecards, underwriting)
• Experience in fintech, lending, payments, fraud, or insurance
• Experience building ML platform components like feature stores or model registries
• Backend engineering depth with production services and APIs
• Experience with LLMs and agentic systems applied to operational problems
• Experience as a founding ML hire or building ML infrastructure from scratch
• Advanced degree (MS or PhD) in a quantitative field