3 дня назад
Head of Risk, Decision Science & Portfolio Strategy (Fintech)
270 000 - 300 000$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Head of Risk, Decision Science & Portfolio Strategy (Fintech): Owning Pipe's end-to-end credit strategy, risk infrastructure, model deployment, underwriting policies, and portfolio performance with an accent on data integrity, machine learning decisioning, and alternative data acquisition. Focus on building monitoring and early-warning systems, translating models into pricing and approval frameworks, and leading seven risk specialists across a high-growth lending platform.
Location: Remote, United States
Annual US base salary: $270,000–$300,000
Company
builds embedded capital and credit products for small businesses through vertical SaaS platforms, payments platforms, and marketplaces.
What you will do
- Own the performance, deployment, monitoring, governance, and validation of statistical and machine learning models used for credit decisioning.
- Direct credit strategy, including underwriting policies, approval logic, pre-qualification, credit limits, pricing, and risk appetite criteria.
- Monitor portfolio performance, build early-warning systems, and drive timely strategy adjustments across credit products.
- Own the data quality roadmap, identify missing signals, source new internal or third-party data, and define data infrastructure requirements.
- Work with Risk Operations, Engineering, Product, Capital Markets, and Finance on production implementation, loss forecasting, reserves, compliance, and facility covenants.
- Lead and develop a team of seven risk specialists responsible for modeling, strategy translation, and portfolio monitoring.
Requirements
- 8+ years of experience in credit risk, including work with fintech, marketplace lending, banking, consumer lending, or SMB lending products.
- Deep expertise in credit strategy for MCA or unsecured loan products.
- Hands-on experience with machine learning and statistical modeling for credit decisioning, including gradient boosting, logistic regression, or survival models.
- Experience translating model outputs into underwriting policies, approval logic, and pricing frameworks that improve portfolio results.
- Experience building monitoring frameworks, early-warning systems, and feedback loops between strategy and outcomes.
- Fluency in Python and SQL, willingness to debug raw data logs, and experience sourcing and integrating new data for credit performance.
Nice to have
- Experience with embedded finance, B2B2C distribution models, or platform-based lending.
- Familiarity with bank transaction, payment processing, or SaaS MRR data for underwriting.
- Experience with credit products for underserved or thin-file borrowers.
- Proficiency in R for analysis and model development.
Culture & Benefits
- Fully remote work with flexible vacation and working hours.
- Health, dental, and vision insurance.
- Generous parental leave regardless of gender.
- Company-provided equipment.
- Culture focused on authenticity, humility, excellence, and a sustainable pace of execution.
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