Data Scientist, Credit Risk Analytics

Prosper Marketplace

Data Scientist building and deploying credit and fraud risk models for Prosper’s digital personal-finance products. Analyzing portfolio performance and shaping credit-risk strategies with machine learning, Python, and SQL.

Posted 8/5/2026full-timeArizona, California • 🇺🇸 United StatesJuniorMid-Level💰 $129,000 - $179,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning models for credit and fraud risk management, utilizing statistical programming languages like Python and SQL. Capable of analyzing portfolio performance and translating complex data insights into actionable business strategies while ensuring compliance with regulatory guidelines.

Highest-signal resume keywords

Machine Learning Model DevelopmentStatistical Programming (Python)Database Management (SQL)Credit Risk StrategyConsumer Lending Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical AnalysisData AnalysisRoot-Cause AnalysisModel DocumentationML Ops PrinciplesPredictive TechniquesData Science Productivity ToolsWorkflow SolutionsCredit Bureau Reports

Soft Skills

CollaborationCommunicationSelf-MotivationResults OrientationCreativity

Certifications & Qualifications

Advanced Degree (M.S./Ph.D.)

Industry Keywords

FintechFinanceCredit Risk ManagementFraud Risk ManagementConsumer LendingUnsecured Personal LoansCredit CardsRegulatory ComplianceOperational EfficiencyBusiness Strategy

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

Build machine learning models for credit and fraud risk management and collaborate with engineering to deploy them in production
Use credit bureau reports and customer-supplied information at scale to develop credit and fraud strategies
Propose and execute strategic solutions to complex business problems aligned with company objectives
Analyze portfolio performance at granular segment levels, identify trends, conduct root-cause analysis, and communicate recommendations
Develop internal tools and workflow solutions to improve data science productivity and operational efficiency
Monitor production credit risk models and strategies and extract actionable insights
Assess new machine learning algorithms and features from alternative data providers
Conduct ad-hoc analyses supporting risk management, investor services, operations, and corporate development
Shape credit risk strategy and business decisions
Participate in recruiter, department, team/virtual, and final-round interviews

Requirements

What you’ll need

2–3+ years of work experience in fintech, finance, or another high-impact field applying statistical and machine learning predictive techniques
Consumer lending experience in unsecured personal loans or credit cards is a strong plus
Advanced degree (M.S./Ph.D.) preferably in statistics, computer science, engineering, physical sciences, economics, or a related technical field
Expert knowledge of statistical programming languages such as Python
Expert knowledge of database languages such as SQL
Solid understanding of coding best practices, model documentation, and ML ops principles
Ability to translate complex technical subject matter into clear, actionable business strategies
Ability to collaborate across engineering, product, and compliance functions
Ability to work unsupervised in a fast-paced environment and prioritize parallel projects
Ability to innovate within regulatory guidelines
Commitment to reproducible research and model governance
Self-motivated, results-oriented, enthusiastic, and creative approach

Benefits

Comp & perks

A connected experience with high-touch collaboration and flexibility
Digital-first tools and intentional culture
Competitive salary
401(k) with a 5% company match
Flexible time off
Paid parental leave
Annual wellness allowance
Comprehensive health coverage
Udemy access
Childcare assistance
Pet insurance
Additional savings through Beneplace
Bonus