Associate Fraud Strategy Data Scientist at BILL in San Jose, CA

Summary: BILL is a Fintech company that empowers businesses by replacing outdated financial processes with innovative tools. They are seeking an Associate Fraud Strategy Data Scientist to support projects related to fraud detection, risk analysis, and loss mitigation through data science and analytics. Responsibilities: - Support key projects associated with fraud detection, risk analysis and loss mitigation - Achieve business goals through design, creation, and execution of control strategies - Develop and maintain risk strategy frameworks to keep model strategy up to date with high performance - Build and deploy data driven and automated monitoring rules to detect and quickly respond to evolving risk trends - Utilize advanced analytics techniques to contribute to the refinement of end to end control strategies - Develop flexible performance dashboards and monitoring that fit business needs - Apply advanced knowledge of data, metrics, profiles/typologies and key indicators in the financial fraud risk domain - Identify and execute new model/rules/product opportunities to optimize processes aligning with business goals - Partner and collaborate with cross functional teams to design strategies across the lifecycle - Establish business requirements, shared KPIs, guiding execution, and performing validation/maintenance - Mentor and support junior team members to achieve goals - Apply AI to accelerate data science work by designing prompts and integrating LLMs into automated pipelines Required Qualifications: - 0 - 2 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 - Achieved 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 - Developed and maintained risk strategy frameworks for a domain to keep model strategy up to date with high performance with the goal of delivering on KPIs - Built and deployed data driven and automated monitoring rules to detect and quickly respond to evolving risk trends - Partnered with product/engineering on product/customer touchpoints for risk signal capture and treatments from strategies - Utilized 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 - Expertise with interpreting results and using data findings to influence decision making - Developed 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 - Hands on experience wrangling complex data in tools (ie. Tableau) with the focus to perform monitoring, diagnostic analytics, and share actionable stories with data - Applied advanced knowledge of data, metrics, profiles/typologies and key indicators in the financial fraud risk domain - Demonstrated ability to find and recommend additional enhancements within data features, data enrichment, score recalibration for existing strategies and processes - Identify and execute new model/rules/product opportunities in order to optimize processes aligning with the business goals - 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 - Experience influencing cross functional team approaches - Mentorship and support of junior team members to achieve goals - Experience applying AI to accelerate data science work by designing prompts, rigorously evaluating outputs, and integrating LLMs through APIs into notebooks and automated pipelines Preferred Qualifications: - 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 Required Skills: Fraud risk control strategy, Data science, Predictive algorithms, SQL, Python, Risk strategy frameworks, Data monitoring, diagnostics, Tableau, Financial fraud risk domain knowledge, AI, LLM integration, Experimental design, Mentorship 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