Senior Data Scientist – Credit and Lending ML

Mercury

. Build and own models behind the lending program, including transaction classification, recurring inflow/outflow detection, annual revenue estimation, seasonal forecasting, and anomaly detection .

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

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models for lending programs, with a strong foundation in Python and SQL. Capable of collaborating with cross-functional teams to enhance underwriting processes and communicate complex models effectively.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingSQL ProficiencyLending Algorithms ExperienceDeep Learning Exposure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel DeploymentData ClassificationAnomaly DetectionRevenue EstimationSeasonal ForecastingPredictive ModelingData AnalysisStatistical ModelingQuantitative Analysis

Soft Skills

CommunicationCollaborationTechnical LeadershipProblem-SolvingModel Explanation

Tools & Technologies

Data WarehouseProduction-Grade ServicesMonitoring ToolsObservability Tools

Industry Keywords

LendingCreditUnderwriting PolicyTransaction ClassificationData Streams

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Build and own models behind the 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 a business's 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 can improve the underwriting program
Set the technical bar for ML in Credit

Requirements

What you’ll need

3+ years building ML models
At least 1 year in lending or credit algorithms
Some exposure to using deep-learning / transformer models in a professional context
Strong in Python, with experience working on production-grade services
Strong in SQL and at home in a data warehouse
Able to explain a model to a credit officer, a model validator, a PM, and an engineer
Able to set modeling standards for data-science colleagues
Quantitative degree, or the equivalent built some other way

Benefits

Comp & perks

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