Staff Data Scientist – Credit and Lending ML

🔥 1 hour ago

🌐 United States, Canada – Remote

🇺🇸 United States – Remote 🇨🇦 Canada – Remote

🏄 California, New York, +1 more states – Remote

🏄 California – Remote 🗽 New York – Remote 🌲 Oregon – Remote

💵 $166.6k - $298.8k / year

⏰ Full Time

🔴 Lead

📊 Data Scientist

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

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Mercury

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2017

💳 Fintech

🏦 Banking

☁️ SaaS

Fintech • Banking • SaaS

Mercury is a software-driven fintech that provides business and personal banking products built for startups and small to mid-size companies. Its platform offers business checking and savings, Treasury products, business credit cards and spend management, payments and invoicing, accounting integrations and AI-powered automations, plus APIs and developer tools. Mercury partners with FDIC-insured banks to offer deposit protection, emphasizes virtual cards, granular spend controls, and workflow automations for teams, agencies, ecommerce brands, VC funds, and crypto businesses. The product is positioned as an all-in-one banking and finance platform delivered through a modern app and API.

📋 Description

• Build and own models behind the small business lending program, including transaction classification, recurring inflow/outflow detection, annual revenue estimation, seasonal forecasting, and anomaly detection • Turn bank transactions, storefront sales, and other messy data streams into a defensible view of business health • Deploy models into production with observability, monitoring, and tests • Partner with Credit on underwriting policy, including requested data, follow-up information, and uncertainty quantification • Partner with Product on principal sizes, pricing, and repayment terms • Assess where predictive modeling advances the underwriting program • Set the technical bar for ML in Credit

🎯 Requirements

• 7+ years building ML models • Implemented models that shaped automated decisions with money on the line • Production-grade Python • Depth beyond classification models, such as time series and forecasting, anomaly detection, and NLP models • Proficiency with deep learning, transformers, and LLMs • Strong SQL skills and comfort working in a data warehouse • Ability to explain a model to a credit officer, model validator, PM, and engineer • Ability to set modeling standards for data-science colleagues • Quantitative degree, or the equivalent built some other way • Direct experience modeling on bank transaction data (nice to have) • Experience in small business lending or business credit cards (nice to have) • Experience with revenue-based financing, merchant cash advances, or similar products (nice to have)

🏖️ Benefits

• Equity (stock options/RSUs) • Benefits • Equal Employment Opportunity employer • Reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs

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