Staff Analyst

Job Description

REQUIREMENTS

• 5+ years of experience in analytics, credit risk, quantitative finance, or a related field within fintech, financial services, consumer lending, or a similar industry.
• Experience with credit risk, loss forecasting, portfolio analytics, valuation, or related financial-performance frameworks.
• Working knowledge of risk capital or structured finance concepts, such as securitizations, warehouse facilities, forward-flow programs, or investor reporting.
• Strong SQL skills and proficiency in at least one programming language, such as Python or R.
• Experience working with large datasets and developing repeatable, well-documented analyses.
• Ability to turn complex analytical work into clear, actionable business insights.
• Strong written and verbal communication skills, with the ability to collaborate effectively across functions.

Preferred

• Experience partnering with Machine Learning or Data Science teams, particularly in underwriting or model-performance monitoring.
• Familiarity with capital analytics, deal economics, and risk/return frameworks.
• Experience building analytics infrastructure, dashboards, automated reporting, or GenAI-enabled analytical solutions.
• Exposure to model governance, controls, audit, model validation, or valuation review processes.
• Demonstrated ability to manage analytical projects, influence stakeholders, and drive alignment.

RESPONSIBILITES

• Develop and maintain credit analytics used for portfolio monitoring, forecasting, valuation, and decision-making.
• Support loss forecasting, scenario analysis, portfolio performance measurement, and related reporting.
• Contribute to risk capital analytics for funding strategies, including securitizations, warehouse facilities, and forward-flow programs.
• Analyze portfolio trends and translate findings into clear recommendations for product, credit policy, finance, and funding stakeholders.
• Partner with cross-functional teams to align on definitions, metrics, analytical frameworks, and business requirements.
• Build and improve data models, pipelines, dashboards, and automated reporting to enable reliable, self-serve insights.
• Establish strong practices for analytical validation, monitoring, documentation, and governance.
• Communicate complex analyses clearly to both technical and non-technical audiences.

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