Develop and own the analytical framework for credit risk, including underwriting models, loss forecasting, and credit policy definition using SQL and Python.
Construct comprehensive dashboards (Sigma, Looker, or similar) to track repayment behavior, vintage performance, delinquency roll rates, and unit economics.
Analyze product performance to recommend data-backed changes to product designs that maximize funding availability while protecting downside.
Conduct deep-dive assessments on market segments and risk profiles to help design and launch future financial products for real estate agents.
Leverage AI tools (ChatGPT, GitHub Copilot) to accelerate code generation and insight discovery, moving our risk processes from manual reviews to automated decisioning.
Partner closely with Product, Finance, and Operations leadership to translate complex risk data into clear, actionable business strategies.

Bachelor’s degree in a quantitative or analytical field (e.g., Economics, Finance, Mathematics, Statistics, Engineering, or Computer Science).
3+ years of experience in credit risk, analytics, fintech lending, specialty finance, or adjacent fields.
Experience in a high-growth startup or fintech environment is a strong plus.
Strong proficiency in SQL and Python is required; you must be comfortable querying raw data and building your own analytical models.
A solid grasp of credit risk principles for SMB or consumer lending, including repayment modeling, vintage analysis, and concentration risk.
Experience building dashboards and analytical tools using platforms like Sigma, Looker, Tableau, or Metabase.
Ability to utilize AI tools to dramatically increase speed and output quality in a lean environment.
You thrive in ambiguity and are energized by building processes from scratch rather than just managing existing ones.
Exceptional ability to simplify complex quantitative findings into clear written and verbal narratives for executive stakeholders.

Credit Risk Manager

Key skills