Fraud Strategy Data Scientist

BILL•San Jose, CA

19h•$95,800 - $135,000•Remote

About The Position

We are looking for a talented, enthusiastic and dedicated person to join BILL’s Fraud Risk Strategy team. The incumbent will be responsible for leading key projects associated with fraud detection, risk analysis and loss mitigation at Bill.com. This position requires a person who has experience with performing analytics, refining risk strategies, and developing predictive algorithms preferably in the risk domain.

Requirements

Minimum 3+ years of experience in end to end fraud risk control strategy experience within relevant industry experience in eCommerce, or online payments, leveraging data science/analytics to solve complex business problems.
Experience building complex SQL/Python scripts with minimal guidance to solve ambiguous problems.
Hands on experience wrangling complex data in tools (ie. Tableau) with the focus to perform monitoring, diagnostic analytics, and share actionable stories with data.
Experience in project leadership, partnering and collaborating with cross functional teams including modeling, product/engineering, operations to effectively design strategies across the lifecycle at multiple touchpoints.
Experience applying AI to accelerate data science work by designing prompts, rigorously evaluating outputs, and integrating LLMs through APIs into notebooks and automated pipelines.
Experience in experimental design, fraud typologies that involve onboarding fraud/abuse, and data/control governance, including proposal development, user acceptance definition, pre/post implementation validation, and approval workflows to ensure high quality deployments.
Authorization to work in the United States without requiring visa sponsorship now or in the future.

Responsibilities

Leading key projects associated with fraud detection, risk analysis and loss mitigation at Bill.com.
Performing analytics, refining risk strategies, and developing predictive algorithms preferably in the risk domain.
Achieving ambitious business goals through design, creation, and execution of control strategies through direct work (complex analytical rule development, maintenance, etc) and in collaboration with Product Managers, Engineers, and Business Stakeholders.
Developing, maintaining, and refining risk strategy frameworks for a domain to keep model strategy up to date with high performance with the goal of delivering on KPIs.
Building and deploying data driven and automated monitoring rules to detect and quickly respond to evolving risk trends.
Partnering with product/engineering on product/customer touchpoints for risk signal capture and treatments from strategies.
Utilizing advanced analytics techniques to significantly contribute to the refinement of end to end control strategies, including experience in building complex SQL/Python scripts with minimal guidance to solve ambiguous problems.
Interpreting results and using data findings to influence decision making.
Developing flexible performance dashboards and monitoring that drill to the right level of granularity to fit the audience, business needs; covering the breadth of control strategy.
Performing monitoring, diagnostic analytics, and sharing actionable stories with data.
Applying advanced knowledge of data, metrics, profiles/typologies and key indicators in the financial fraud risk domain.
Finding and recommending additional enhancements within data features, data enrichment, score recalibration for existing strategies and processes.
Identifying and executing new model/rules/product opportunities in order to optimize processes aligning with the business goals.
Leading projects, partnering and collaborating with cross functional teams including modeling, product/engineering, operations to effectively design strategies across the lifecycle at multiple touchpoints.
Establishing business requirements, shared KPIs, guiding execution, and performing validation/maintenance.
Influencing cross functional team approaches.
Mentoring and supporting junior team members to achieve goals.
Applying AI to accelerate data science work by designing prompts, rigorously evaluating outputs, and integrating LLMs through APIs into notebooks and automated pipelines.
Experience in experimental design, fraud typologies that involve onboarding fraud/abuse, and data/control governance, including proposal development, user acceptance definition, pre/post implementation validation, and approval workflows to ensure high quality deployments.