Senior Lead Product Manager - Decisioning Platform

Responsibilities

• Own the product vision, strategy, and roadmap for BILL's core risk decisioning and policy engine, enabling real-time, low-latency automated evaluations across fraud, credit, and compliance use cases.
• Define the technical architecture for internal APIs, platform services, and data pipelines that power risk evaluation, ensuring the platform scales reliably with BILL's growing transaction volume and product surface.
• Partner with data engineering to design and optimize data strategies across BILL's Data Lake and high-throughput data platforms, structuring data for advanced risk modeling and analytics at scale.
• Evaluate, integrate, and orchestrate third-party identity, financial, and alternative data sources, building a signal ecosystem that maximizes decision accuracy while managing cost, latency, and vendor risk.
• Serve as the translation layer between risk policy goals and deep engineering execution, working directly with data scientists, security engineers, and data architects to align technical implementation with business outcomes.
• Define and track platform-level success metrics including decisioning latency, signal coverage, model input quality, and platform uptime, using data to drive prioritization and surface tradeoffs to leadership.
• Identify and drive technical debt reduction, scalability investments, and architectural improvements that enable the risk platform to support new products, geographies, and regulatory requirements.

Requirements

• 8 or more years of technical platform / product management experience, with a track record of shipping scalable internal infrastructure: decisioning platforms, rules engines, orchestration layers, or high-throughput data pipelines in a fintech or payments environment.
• Deep technical fluency in data infrastructure, including hands-on experience defining requirements for Data Lakes, data warehouse architectures, and real-time or batch data processing systems at scale.
• Extensive experience evaluating and integrating third-party data APIs, including identity, financial, and alternative data providers, and building orchestration logic that balances signal diversity, latency, and cost.
• Strong understanding of how data infrastructure decisions directly affect fraud mitigation, credit risk outcomes, and compliance logic, with the ability to reason across all three domains simultaneously.
• Comfort working directly with engineers at the level of API schemas, data models, and system architecture diagrams, translating between technical implementation and product strategy in both directions.
• Experience defining and owning platform-level metrics, including latency, throughput, data quality, and decisioning accuracy, and using those metrics to drive roadmap investment decisions.
• Fintech Domain Expertise: 15+ years architecting complex end-to-end fraud, compliance, and credit risk management systems within financial technology platforms.
• Sanctions & Compliance: Deep experience implementing real-time sanctions screening protocols and regulatory compliance mechanisms.
• End-to-End Credit Platform Architecture: Proven capability in building comprehensive credit risk platforms covering automated underwriting, portfolio exposure management, and dynamic credit limit adjudication.
• Data Integration: Hands-on experience integrating traditional credit bureau data and alternative data sources into unified risk and evaluation models.
• Fraud Mitigation: Architected robust, automated fraud engine solutions protecting high-value disbursements ($1B+ in payouts).

Qualifications

• Experience building or modernizing a risk decisioning platform at a company that processes significant payment or lending volume, with direct exposure to the tradeoffs between build, buy, and integrate.
• Familiarity with machine learning model integration into production decisioning systems, including feature engineering, model serving, and monitoring for drift or degradation.
• Proven experience building high-throughput feature engineering pipelines utilizing distributed streaming and big data processing technologies.
• Demonstrated ability to drive measurable business outcomes through feature engineering (e.g., improving fraud model efficiency by 15% and cutting false positives by 10%).
• Pioneer in leveraging LLM-powered agents to automate and orchestrate end-to-end feature engineering and data workflows.
• Hands-on technical fluency: Deep, practical command over the data architecture, streaming frameworks, and ML pipeline infrastructure required for real-time risk decisioning engines.

Benefits

• 100% paid employee health, dental, and vision plans (choose HMO, PPO, or HDHP)
• HSA & FSA accounts
• Life Insurance, Long & Short-term disability coverage
• Employee Assistance Program (EAP)
• 11+ Observed holidays and wellness days and flexible time off
• Employee Stock Purchase Program with employee discounts
• Wellness & Fitness initiatives
• Employee recognition and referral programs