Staff Data Scientist – Credit and Lending ML

Mercury

. 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 .

Posted 11/4/2026full-timeRemote • United States, CanadaLead💰 $166,600 - $298,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in building and deploying machine learning models for small business lending, with a focus on transaction classification, forecasting, and anomaly detection. Strong proficiency in Python and SQL, along with the ability to communicate complex models to diverse stakeholders.

Highest-signal resume keywords

Machine Learning Model DevelopmentProduction-Grade PythonTime Series ForecastingDeep Learning and NLPSQL Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DeploymentAnomaly DetectionTransaction ClassificationRevenue EstimationSeasonal ForecastingPredictive ModelingData AnalysisQuantitative AnalysisModel Validation

Soft Skills

CommunicationCollaborationProblem-SolvingLeadership

Tools & Technologies

Data WarehouseTransformersLarge Language Models

Industry Keywords

Small Business LendingBusiness Credit CardsRevenue-Based FinancingMerchant Cash AdvancesUnderwriting Policy

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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