Head of Risk, Decision Science – Portfolio Strategy

This is a fully remote position, open to applicants in United States.

📋 Description

• Take responsibility for the performance and outcomes of all deployed statistical and machine learning models, ensuring a smooth transition from data science results to effective underwriting decisions and quantifiable portfolio outcomes.

• Implement rigorous monitoring and feedback mechanisms between model outputs and real-world credit results.

• Promote the use of alternative data (transaction-level signals, partner platform information, behavioral indicators) to enhance model accuracy and increase approval rates.

• Oversee model risk governance, which includes model documentation, validation processes, and continuous performance standards for all deployed models.

• Lead the comprehensive credit strategy, converting risk models and data insights into a unified set of underwriting policies, approval strategies, and pricing frameworks aimed at achieving targeted loss and growth performance.

• Manage approval logic, pre-qualification strategies, and credit limit frameworks that balance growth targets, merchant experience, and credit loss thresholds.

• Establish minimum acceptance criteria that align with Pipe's risk appetite while maximizing approval rates, customer lifetime value, and capital facility performance.

• Develop and enforce pricing frameworks to assure adequate loss coverage and positive borrower selection.

• Oversee portfolio performance reporting and forward-looking risk adjustments for all credit products.

• Create early-warning systems and structured decision-making processes that facilitate swift, disciplined adjustments to credit strategies.

• Ensure portfolio resilience through economic cycles by engaging in proactive monitoring and timely interventions.

• Serve as the primary custodian of our data quality roadmap, pinpointing discrepancies and driving technical solutions for resolution.

• Proactively seek and incorporate new data sources (internal or external) to address missing signals and enhance model accuracy.

• Convert business requirements into precise data engineering and infrastructure specifications, ensuring the availability of reliable, scalable inputs for risk decision-making.

• Collaborate closely with Risk Operations to ensure the dependable implementation of credit strategies in production systems and rapid iteration based on portfolio outcomes.

• Work alongside Capital Markets and Finance on loss forecasting, reserve setting, and risk appetite frameworks; manage credit policies to ensure compliance with facility covenants and triggers.

• Team up with Product and Engineering to develop scalable decision-making infrastructure that supports quick strategy iterations.

• Lead a team of seven risk specialists focused on model development, credit strategy translation, and portfolio monitoring.

• Foster clarity, momentum, and accountability while maintaining a sustainable execution pace.

• Cultivate a culture characterized by analytical rigor, pragmatism, and a bias for action.

⛳️ Requirements

• 8+ years of experience in credit risk, with significant exposure in a fintech, marketplace lender, or bank specializing in consumer or SMB lending products.

• Extensive knowledge of credit strategy for MCA or unsecured loan products.

• Practical experience in machine learning and statistical modeling for credit decision-making (gradient boosting, logistic regression, survival models, etc.).

• Proven capability to convert model outputs into underwriting policies, approval logic, and pricing frameworks that influence portfolio results.

• Demonstrated history of enhancing portfolio performance with speed and accountability in early-stage or high-growth settings.

• Experience in developing monitoring frameworks, early-warning systems, and feedback loops between strategy and outcomes.

• Strong cross-functional instincts: ability to collaborate effectively with Risk Ops, Engineering, Product, Capital Markets, and Finance.

• A "first-principles" operator mindset: you view data quality issues as critical production incidents that you actively troubleshoot rather than delegate.

• Deep familiarity with the tech stack: fluent in Python/SQL and willing to analyze raw data logs to identify anomalies.

• Strategic data ownership: proven success not only in optimizing existing models but also in sourcing and integrating new data necessary for improved credit performance.

🏝️ Benefits

• The best equipment to support your work.

• Flexible vacation and work hours. We prioritize a healthy work-life balance (truly!).

• Comprehensive health, dental, and vision insurance.

• Generous parental leave for anyone expanding their family, regardless of gender.

• Wonderful colleagues! We cherish a culture of authenticity, humility, and excellence, and we want you to leave your mark on our culture.