Role Description We are looking for a talented, enthusiastic and dedicated person to join Bill.com’s Risk Strategy team. This role will report to the 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 collaborating 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. Qualifications - 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 - 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 and manage multiple projects at once - Ability to collaborate effectively with Product Management, Engineering teams, Customer success and sales teams - Extensive knowledge and experience with defining, developing, and deploying risk management methodologies and models - Expertise in driving operational efficiencies and scale through ongoing policy and model optimization - Strong analytical abilities; familiarity with typical credit data, systems & technologies is highly valuable - Hands-on experience leveraging AI/ML tools to strengthen fraud strategy - Familiarity with AI-assisted analytics platforms (e.g., Python-based ML libraries, AutoML tools, or vendor AI solutions) - Demonstrated ability to evaluate and govern AI-generated outputs within a fraud risk context Requirements - 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 - Familiarity with AI-assisted analytics platforms 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 - And much more

• Join a global enterprise SaaS organisation to help build and scale a data-driven marketing analytics capability. • Lead the development of standardised reporting, dashboards, and performance frameworks that provide a trusted view of marketing performance across regions, business units, and channels. • Drive the evolution from reactive reporting to proactive, insight-led analytics that supports executive decision-making. • Partner with cross-functional teams to improve reporting, data quality, governance, and self-service analytics capabilities. • Lead the development and delivery of executive dashboards, scorecards, and marketing performance reporting. • Ensure reporting is accurate, timely, standardised, and aligned to agreed KPIs. • Reduce manual reporting through automation, reusable reporting frameworks, and AI-enabled processes. • Support the development of a centralised analytics environment using Microsoft Fabric and Power BI. • Consolidate fragmented reporting into scalable, standardised dashboards and templates. • Develop and maintain a central marketing KPI and metrics dictionary. • Implement data validation processes and improve data quality across marketing systems. • Support data governance and standard tracking frameworks. • Enable self-service analytics by creating documentation, templates, and user training. • Analyse marketing performance trends and deliver actionable recommendations to stakeholders. • Provide insight across digital marketing, brand performance, and communications effectiveness. • Collaborate across global teams to establish consistent reporting standards and shared analytics capabilities.

• Define the end-to-end methodology every team follows - hypothesis → metrics → design → power → readout → decision - and make it the default • Own the statistical approach (significance, multiple comparisons, sequential testing, variance reduction like CUPED) for small-sample, fast-paced contexts where classic A/B power is hard to reach • Build the methods toolkit for our clustered, hierarchical data (user → sub-account/location → agency), where randomization and analysis units differ • Apply rigorous causal inference (matching, diff-in-diff, instrumental variables, synthetic control, etc) when clean experiments aren't feasible - churn, onboarding, GTM - separating real signal from selection bias, seasonality, and mix effects • Own the design discipline for running many experiments at once - layering, orthogonal experiments, holdouts, and guardrails that keep concurrent tests from contaminating each other • Partner with AI/ML teams to design and evaluate experiments for AI features, including measurement for non-deterministic, fast-iterating systems • Run the experiment review forum and hold the line on what counts as a real result • Build the Experimentation curriculum and templates that level up PMs and analysts so good design scales beyond you • Partner with Analytics Engineering on governed, experiment-ready data and consistent metric definitions • Influence leadership and cross-functional partners on where to invest, translating statistical nuance into clear, decision-grade guidance

• Develop, test and productionize attribution and predictive algorithms by using state of the art machine learning algorithms and optimisation models • Contribute to one or more of the following areas: attribution modeling, CAC/mCAC modeling, LTV prediction, marketing mix modeling and multichannel attribution and testing • Design experiments and interpret the results to draw detailed and actionable conclusions • Work in cross-functional teams across disciplines such as product, engineering and business. • Develop, test and own our production marketing attribution models for better budgeting and increased marketing efficiency