Pipe Technologies

Head of Risk, Decision Science & Portfolio Strategy

Remote, United States

Sponsorship not specified$270k-$300kDetected 14 days ago

PythonSQLMachine LearningData EngineeringData ScienceStatisticsForecastingLeadershipUnderwriting

About the role

We're looking for a Head of Risk, Decision Science & Portfolio Strategy leader to take end-to-end ownership of Pipe's credit strategy and risk outcomes.
This is a senior, hands-on leadership role.
You're comfortable rolling up your sleeves, working through imperfect data and systems, and driving accountability without burning out the team.

Drive use of alternative data (transaction-level signals, partner platform data, behavioral indicators) to improve model accuracy and expand approval rates.
Own model risk governance including model documentation, validation processes, and ongoing performance standards across all deployed models.
Direct the end-to-end credit strategy, translating risk models and data insights into a cohesive set of underwriting policies, approval strategies, and pricing frameworks to deliver target loss and growth performance.
Own approval logic, pre-qualification strategies, and credit limit frameworks that balance growth targets, merchant experience, and credit loss thresholds.
Own portfolio performance reporting and forward-looking risk adjustments across all credit products.
Build early-warning systems and structured decision processes that enable fast, disciplined credit strategy adjustments.

8+ years in credit risk, with meaningful experience at a fintech, marketplace lender, or bank with consumer or SMB lending products.
Deep expertise in credit strategy across MCA or unsecured loan products.
Track record of improving portfolio performance with speed and accountability in early-stage or high-growth environments.
Deep comfort in the stack: you are fluent in Python/SQL and willing to dive into raw data logs to diagnose anomalies.
Strategic data ownership: demonstrated success not just in optimizing existing models, but in sourcing and integrating the net-new data required to unlock better credit performance.

Experience with embedded finance, B2B2C distribution models, or platform-based lending.
Familiarity with alternative data sources (bank transaction data, payment processing data, SaaS MRR) for underwriting.
Background in credit for underserved or thin-file borrowers.
Proficiency in Python, SQL, or R for hands-on analysis and model work.

Flexible vacation and work hours.
Excellent health, dental, and vision insurance.
Generous parental leave for anyone who is growing their family, regardless of gender.
Own the performance and outcome of all deployed statistical and machine learning models, ensuring a seamless translation from data science output to effective underwriting decisions and measurable portfolio results.
Design and implement pricing frameworks to ensure appropriate loss coverage and positive borrower selection.

This listing is sourced directly from Pipe Technologies's careers page and normalized into a canonical job model.