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BILL · 10 hours ago

Fraud Strategy Data Scientist

San Jose, California, United States

Full-time

Hybrid

Mid, Senior Level

$110K/yr - $132K/yr

3+ years exp

BILL is a rapidly growing Fintech company focused on empowering businesses through innovative financial tools. The Fraud Strategy Data Scientist will lead projects in fraud detection and risk analysis, utilizing advanced analytics to develop and refine risk strategies, while collaborating with various teams to optimize processes.

AppsBillingFinancial ServicesFinTechSaaS

No H1B

Responsibilities

Lead key projects associated with fraud detection, risk analysis and loss mitigation at Bill.com

Perform analytics, refine risk strategies, and develop predictive algorithms in the risk domain

Achieve business goals through design, creation, and execution of control strategies

Develop, maintain, and refine 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 respond to evolving risk trends

Partner with product/engineering on product/customer touchpoints for risk signal capture and treatments from strategies

Utilize advanced analytics techniques to refine control strategies, including building complex SQL/Python scripts

Develop flexible performance dashboards and monitoring to fit business needs

Wrangle complex data in tools (ie. Tableau) for monitoring and diagnostic analytics

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

Lead projects, collaborate with cross functional teams, and establish business requirements and shared KPIs

Mentor and support junior team members to achieve goals

Qualification

Fraud risk control strategyData science/analyticsSQLPythonTableauProject leadershipMentorshipCollaborationCommunicationProblem-solving

Required

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

Achieved 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

Developed, maintained, and refined 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

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

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

Preferred

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