Senior / Staff Data Scientist - Credit ML

As a Senior / Staff Data Scientist within the Credit team, you will design, build, and deploy the core inference layer behind Mercury's small business lending platform. Partnering directly with Credit, Product, and Engineering leads, you will turn messy transactional data streams into defensible underwriting decisions. You will own the full ML lifecycle—from revenue estimation, time-series forecasting, and anomaly detection to shipping production-grade Python models with full observability, monitoring, and automated test coverage.

Key Responsibilities

• Lending Inference Engine: Architect, train, and own production machine learning models backing Mercury's lending operations—including transaction classification, recurring inflow/outflow detection, annual revenue estimation, seasonal forecasting, and anomaly detection.
• Data Stream Feature Engineering: Transform messy, high-volume financial data streams (bank transactions, storefront sales, accounting records) into robust indicators of business creditworthiness.
• Production MLOps Deployment: Deploy ML models into production environments complete with automated testing, continuous observability, drift monitoring, and robust documentation.
• Underwriting Policy & Credit Alignment: Partner with Credit leads to define data ingestion policies, quantify model uncertainty, and evaluate when requesting follow-up applicant information justifies friction.
• Product Strategy Collaboration: Partner with Product Managers to shape lending product parameters—including principal sizes, risk-based pricing structures, and repayment schedules.
• Technical Standards & Mentorship: Set the technical bar for machine learning excellence across the Data Science team, establishing modeling guidelines, validation frameworks, and documentation standards.

Requirements

Machine Learning & Data Engineering Depth

• Experience Track: 7+ years of professional experience building, training, evaluating, and deploying production ML models.
• Automated Financial Impact: Proven track record implementing models that drive automated decision-making processes with significant financial capital on the line.
• Production Python Mastery: Strong capability writing clean, modular, production-grade Python code.
• Modeling Breadth Beyond Classification: Deep experience across quantitative domains—specifically time series analysis, forecasting, anomaly detection, NLP, and deep learning architectures (Transformers / LLMs).
• Data Warehousing & SQL: Advanced proficiency with SQL and operating within cloud data warehouse environments (e.g., Snowflake, BigQuery).
• Stakeholder Communication: Outstanding ability to explain complex model architectures, loss functions, and probability distributions clearly to Credit Officers, Model Validators, Product Managers, and Engineers.
• Quantitative Foundation: Degree in a quantitative discipline (Computer Science, Statistics, Mathematics, Data Science, Physics, Computational Finance) or equivalent practical depth.

Preferred Qualifications

• Direct hands-on experience modeling on bank transaction data or open banking APIs.
• Domain expertise in small business lending, business credit cards, commercial underwriting, or merchant cash advances (MCA).
• Familiarity with revenue-based financing structures and risk-adjusted return calculations.

Total Rewards & Benefits

• Compensation & Equity: Competitive base salary ranges ($166,600–$298,800 USD in US / $157,400–$282,400 CAD in Canada) paired with generous equity options/RSUs.
• Health & Wellness: Comprehensive medical, dental, and vision insurance coverage options.
• Remote Culture & Flexibility: Remote-first work model supporting employees across any US or Canadian location.
• Inclusive Work Environment: Reasonable accommodation support throughout the hiring process and dedicated professional growth budgets.