Senior Manager, Risk Strategy Data Scientist
BILL
| Company | BILL |
| Category | Data & Analytics |
| Location | United States |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Manager |
| Salary | Not stated by the employer |
| Posted | 20 Jul 2026 |
| Last verified | 7 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Innovate with purpose
At BILL, we believe in empowering the businesses that drive our economy. By replacing outdated financial processes with innovative tools, we help businesses—from startups to established brands—make smarter decisions and gain control of their operations. And we don’t stop there: we’re creating the future of financial automation so businesses can spend more time on what matters.
Working here means you become part of a vision-driven team that’s ready to tackle challenges and build cutting-edge solutions. We value purpose, drive, and curiosity—and we thrive in a fast-paced, ever-changing environment. Whether in one of our offices in San Jose, CA, Draper, UT, or in a remote-eligible role, BILLders collaborate to deliver real impact for businesses that need more time in their busy weeks.
BILL builds high performing teams and we seek to hire the best talent for every role. We're committed to building a workplace that fosters inclusion and diverse perspectives, valuing each person’s unique skills and experiences. We’d love to hear from you—you might be just what we’re looking for, whether in this role or another.
✨ Let’s give businesses more time for what matters. Make your impact within a rapidly growing Fintech Company
We are looking for a talented, enthusiastic and dedicated person to join Bill.com’s Risk Strategy team. This role will report to VP of Fraud Risk Strategy and will work closely with cross functional teams to formulate fraud and credit risk management strategies and controls for payments across different channels. This person will be responsible for building and maintaining the risk strategies and collaborate with cross functional teams to operationalize these risk strategies. This position requires a person who has experience with using data to inform insights, influence stakeholders on relative priorities, management, refining risk strategies and driving initiatives.
We’d love to chat if you have:
8 years of experience in risk management within the Fintech or financial services industries with an emphasis on fraud and credit risks and 3 - 5 years in management
Bachelor's degree; MS/MA/MBA degree preferred in Business, Economics, Finance, Analytics, Mathematics, or a related field
Strong knowledge of the fundamentals of risk strategy such as fraud detection and prevention, balancing customer experience against rising fraud threats, using data to draw insights for risk mitigation
Adept at SQL queries, reporting and presenting findings; Proficiency in Excel and other visualization tools
Strong organization and time management skills and the ability to prioritize manage multiple projects at once
Ability to collaborate effectively with Product Management, Engineering teams to convey the strategy and work through the roadmap prioritization, planning and implementation. Also collaborating effectively with Customer success and sales teams while dealing with escalations and effectively communicating the risk policies to the broader organization.
Extensive knowledge and experience with defining, developing, and deploying risk management methodologies and models including data science and rule-based and predictive fraud models
Expertise in driving operational efficiencies and scale through ongoing policy and model optimization.
Communicate and liaise effectively with the Compliance team and formulate the strategy for new product launches.
Experience managing cross-functional, multi-stakeholder processes, communicating and influencing stakeholders at various levels across functional boundaries
Strong analytical abilities. Familiarity with typical credit data, systems & technologies is highly valuable
Hands-on experience leveraging AI/ML tools to strengthen fraud strategy , including using LLM-based signals, anomaly detection, or generative AI to identify emerging fraud patterns and inform rule and model development
Famili