The business problem involves mitigating fraud and risk in the e-commerce and online payments industry by leveraging data science, machine learning, and AI to detect and prevent fraud, while ensuring trust and security for end customers.
Requirements
- 2-6 years of experience in machine learning/AI, data science, risk analytics & data analysis within relevant industry experience in eCommerce, online payments, user trust/risk/fraud, or investigation/product abuse
- Proficiency in SQL, Python, AWS, Excel including key data science libraries
- Proficiency in data visualization including Tableau
- Experience working with large datasets
- Experience using statistics and data science (machine learning & AI) to solve complex business problems
- Experience with development and implementation of AI tools (e.g. LLMs) for risk use cases
- Strong SQL proficiency
Responsibilities
- Design and develop machine learning and AI models to detect/mitigate fraud
- Support stakeholders and cross-functional teams in effective usage of models
- Drive AI transformation for all risk management activities at BILL
- Work with product/engineering to implement, monitor and refine AI solutions and models
- Utilize data analysis to design and implement fraud models
- Collaborate with cross-functional stakeholders including product managers and engineering teams to deploy data-driven fraud models and AI solutions that operate at scale and in real time for end customers
- Development of dashboard and visualizations to track KPI of fraud models implemented
Other
- Bachelors/Master's degree in Data Science, Data Analytics, Mathematics, Statistics, Data Mining or related field or equivalent practical experience
- Hybrid position requiring candidates to be based in the San Jose area
- Strong communication skills
- Ability to clearly communicate complex results to technical experts, business partners, and executives
- Comfortable with ambiguity and yet able to steer AI and machine learning projects toward clear business goals, testable hypotheses, and action-oriented outcomes
