Senior Lead Product Manager - Risk Platform

BILL•San Jose, CA

1d•$162,800 - $239,400•Remote

About The Position

The Risk Product team is building the core intelligence layer that keeps BILL's ecosystem safe, scalable, and trustworthy. As Senior Lead Product Manager for the Risk Platform, you will own the technical product strategy for the decisioning and policy engine that powers real-time risk evaluation across credit, fraud, and compliance. This is not a feature team: it is the foundational infrastructure that every risk control at BILL depends on, processing millions of transactions and onboarding decisions for the small and medium-sized businesses that rely on BILL every day. In this role, you will set the roadmap for how BILL ingests, structures, and acts on risk signals at scale. You will define the architecture of our data pipelines and third-party data integrations, partner directly with data engineers, data scientists, and security engineers to translate complex policy requirements into platform capabilities, and ensure the decisioning engine can grow with BILL as transaction volume, product lines, and regulatory obligations expand. Your work will sit at the center of every risk decision BILL makes, from the moment a new business applies to join the platform through every payment they process.

Requirements

8 or more years of 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.

Nice To Haves

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.

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.