Staff Data Scientist - Credit and Lending ML

Wwshemi; San Francisco, New York, Portland (CA, NY, OR); Ibrido; USD 167.000 - 299.000; Tempo pieno

Descrizione del lavoro

Mercury is seeking a Senior/Staff Data Scientist to build and own the inference layer behind our small business lending program. You will ship models to production with strong monitoring and documentation, partner with Credit and Product to shape underwriting decisions, and help set ML standards for the Credit organization.

You will work with transaction data, revenue forecasting, and NLP/detection methods, ensuring models are explainable and production-ready for high-stakes lending decisions.

Competenze

• 7+ years building ML models.

Mansioni

• Build and own the models behind our lending program - transaction classification, recurring inflow/outflow detection, annual revenue estimation, forecasting with seasonality, anomaly detection, and more.
• Turn messy data streams - bank transactions, storefront sales, and more - into a defensible view of a business's health.
• Deploy models into a production environment with observability, monitoring, and tests.
• Partner with Credit on underwriting policy: data asked for, follow-up info value, and quantify uncertainty.
• Partner with Product on product shape: principal sizes, pricing, and repayment terms.
• Assess where predictive modeling actually moves the underwriting program.
• Set the technical bar for ML in Credit.

Conoscenze

ML models

Python

Time series

Forecasting

NLP

Deep learning

SQL

Data warehousing

Model explainability

Communication

Formazione

Quantitative degree

Strumenti

PyTorch

SQL databases

Airflow

Hugging Face

Descrizione del lavoro

Staff Data Scientist - Credit and Lending ML

Mercury Remote - San Francisco, CA, New York, NY, Portland, OR, or within Canada or United States Posted 2026-11-04

This listing's locations Remote - San Francisco, CA, New York, NY, Portland, OR, or within Canada or United States

Workplace Remote

Compensation USD 166600–298800 (period not stated)

Department: Data

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About a week before Hurricane Sandy made landfall, the two leading forecast models flatly disagreed: the American one carried the storm safely out into the Atlantic, the European one turned it hard left into New Jersey. The European model wasn't running a better simulation, it was running an ensemble. Hundreds of thousands of people were evacuated on the strength of the spread in forecast distribution, days before any single forecast could have justified moving them, and on October 29th, 2012, Sandy came ashore almost exactly where the models had put it.

At Mercury, we're building the future of financial infrastructure for startups and growing businesses. We're extending credit to those businesses, and we're doing it with proprietary access to some of the best data for understanding a borrower's business, from bank transactions to financial reporting to payroll. We're looking for a Senior/Staff Data Scientists to turn that record into a lending decision, partnering closely with Credit, Product, and Engineering.

In this role, you'll build and own the inference layer behind our small business lending program; you'll ship it into production with the monitoring and documentation that make it trustworthy; and you'll help Credit and Product decide what we should be lending in the first place.

Here Are Some Things You'll Do On The Job:

• Build and own the models behind our lending program - transaction classification, recurring inflow/outflow detection, annual revenue estimation, forecasting with seasonality, anomaly detection, and more
• Turn messy data streams - bank transactions, storefront sales, and more - into a defensible view of a business's health
• Deploy models into a production environment with observability, monitoring, and tests
• Partner with Credit on underwriting policy: what data is asked for, 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

You Should:

• Have 7+ years building ML models
• Implemented models that shaped automated decisions with money on the line
• Write production-grade Python
• Have depth beyond classification models; for example: time series and forecasting, anomaly detection, and NLP models
• Be proficient with deep learning, transformers, and LLMs
• Be strong in SQL and at home in a data warehouse
• Be able to explain a model to a credit officer, a model validator, a PM, and an engineer
• Be able to set modeling standards for your data-science colleagues
• Have a quantitative degree, or the equivalent built some other way

Nice To Have:

• Experience in small business lending or business credit cards
• Experience with revenue-based financing, merchant cash advances, or similar products

*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.

Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.

Total RewardsThe total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.

Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.

Our target new hire base salary ranges for this role are the following: