Senior/Staff Data Scientist, Lending - Apply Now at Mercury

Mercury is looking for a Senior/Staff Data Scientist to turn business financial data into lending decisions, working closely with Credit, Product, and Engineering. You will build and own the inference layer behind the small business lending program and ship it to production with the monitoring and documentation that make it trustworthy. You will also help Credit and Product decide what the company should be lending in the first place.

Key Responsibilities

• Build and own the models behind the lending program, including transaction classification, recurring inflow/outflow detection, annual revenue estimation, forecasting with seasonality, and anomaly detection.
• Turn messy data streams, such as bank transactions and storefront sales, into a defensible view of a business’s health.
• Deploy models to production with observability, monitoring, and tests.
• Partner with Credit on underwriting policy: what data to request, when follow-up information is worth the friction, and how to quantify uncertainty.
• Partner with Product on the shape of the product, from principal sizes and pricing to repayment terms.
• Assess where predictive modeling actually moves the underwriting program.
• Set the technical bar for ML in Credit.

Requirements

• 3+ years building ML models, including at least 1 year in lending or credit algorithms.
• Some exposure to deep-learning or transformer models in a professional setting.
• Strong Python skills, with experience on production-grade services.
• Strong SQL skills and comfort working in a data warehouse.
• Ability to explain a model to a credit officer, a model validator, a PM, and an engineer.
• Ability to set modeling standards for fellow data scientists.
• A quantitative degree, or equivalent experience built another way.

Skills

Technical Skills

• Machine learning model development
• Credit and lending algorithms
• Python (production-grade services)
• SQL and data warehouses
• Forecasting, anomaly detection, and transaction classification
• Model deployment, monitoring, and testing
• Deep-learning and transformer models (some exposure)

Soft Skills

• Explaining models clearly to technical and non-technical audiences
• Cross-functional partnership with Credit, Product, and Engineering
• Opinionated, thoughtful judgment on where ML adds value
• Setting standards and leading by example

Nice to Have

• Direct experience modeling on bank transaction data.
• Experience in small business lending or business credit cards.
• Experience with revenue-based financing, merchant cash advances, or similar products.

Benefits

• Base salary
• Equity (stock options/RSUs)
• Benefits package (details not itemized in the posting)