Head of Risk, Decision Science – Portfolio Strategy

Pipe

Head of Risk leading end-to-end credit strategy and risk outcomes at Pipe Technologies. Overseeing data integrity, model performance, and team leadership with a focus on SMBs.

Posted 7/8/2026full-timeRemote • 🇺🇸 United StatesLead💰 $270,000 - $300,000 per yearWebsite

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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.
Establish rigorous monitoring and feedback loops between model outputs and real-world credit outcomes.
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.
Set minimum acceptance criteria aligned with Pipe's risk appetite while optimizing approval rates, customer lifetime value, and capital facility performance.
Design and implement pricing frameworks to ensure appropriate loss coverage and positive borrower selection.
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.
Ensure portfolio resilience across economic cycles through proactive monitoring and timely action.
Act as the primary owner of our data quality roadmap, identifying discrepancies and driving technical solutions to resolve them.
Proactively identify and acquire net-new data sources (internal or third-party) to solve for missing signals and improve model accuracy.
Translate business requirements into clear data engineering and infrastructure specs, ensuring we have the right inputs for reliable, scalable risk decisioning.
Work closely with Risk Operations to ensure credit strategy is implemented reliably in production systems and iterated quickly based on portfolio results.
Partner with Capital Markets and Finance on loss forecasting, reserve setting, and risk appetite frameworks; manage credit policy to maintain compliance with facility covenants and triggers.
Collaborate with Product and Engineering to build scalable decisioning infrastructure that enables rapid strategy iteration.
Lead a team of seven risk specialists responsible for model building, credit strategy translation, and portfolio monitoring.
Create clarity, momentum, and accountability while maintaining a sustainable pace of execution.
Build a culture of analytical rigor, pragmatism, and bias for action.

Requirements

What you’ll need

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.
Hands-on background in machine learning and statistical modeling for credit decisioning (gradient boosting, logistic regression, survival models, etc.).
Proven ability to translate model outputs into underwriting policies, approval logic, and pricing frameworks that drive portfolio results.
Track record of improving portfolio performance with speed and accountability in early-stage or high-growth environments.
Experience building monitoring frameworks, early-warning systems, and feedback loops between strategy and outcomes.
Strong cross-functional instincts: able to work effectively with Risk Ops, Engineering, Product, Capital Markets, and Finance.
A "first-principles" operator mindset: you treat data quality issues as critical production incidents you actively debug rather than delegate.
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.

Benefits

Comp & perks

The best equipment to help you do your job.
Flexible vacation and work hours. We believe in a healthy work-life balance (really!)
Excellent health, dental, and vision insurance.
Generous parental leave for anyone who is growing their family, regardless of gender.
Great colleagues! We value a culture of authenticity, humility, and excellence. We want you to make a mark on our culture.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Credit Strategy DevelopmentStatistical ModelingMachine LearningGradient BoostingLogistic RegressionSurvival ModelsData AnalysisPerformance MonitoringRisk AssessmentPortfolio Management

Soft Skills

Analytical RigorAccountabilityProblem-SolvingCollaborationLeadership