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San Jose, California, United States

Internship

Onsite

Intern

BILL is a developer of financial automation software for small and midsize businesses (SMBs). They are seeking detail-oriented and enthusiastic Risk Data Science Interns to work on projects related to fraud and credit risk, focusing on data analysis, machine learning model development, and risk strategy refinement.

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Responsibilities

Collaborate with cross-functional teams, including product management, engineering, and operations, to design, build, and refine data-driven models and strategies for fraud detection and credit risk assessment.
Develop and implement machine learning models and advanced analytics to support risk-related decision-making processes.
Conduct experiments to evaluate the impact of machine learning-driven risk strategies on customer experience.
Analyze large datasets to identify trends and insights, and create visualizations and dashboards using tools like Python, SQL, and Tableau to communicate results to technical and non-technical stakeholders.
Support the development of credit risk strategies by creating predictive algorithms and maintaining high-performance models to achieve key performance indicators (KPIs).
Participate in end-to-end risk control strategy design and execution, including the development of automated monitoring rules and the refinement of existing processes.

Qualification

Required

Currently pursuing a Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Engineering, Economics, or related disciplines, with an expected graduation between December 2025 and August 2026.
Strong foundation in data science and machine learning techniques, including clustering, classification, regression, decision trees, and anomaly detection.
Proficiency in Python, SQL, and data visualization tools (e.g., Tableau) to perform complex data analysis and create actionable insights.
Ability to work with large datasets and apply statistical modeling to solve business problems, particularly in the domains of credit and fraud risk.
Strong analytical thinking, problem-solving skills, and the ability to handle ambiguity while steering projects toward clear, actionable outcomes.
Excellent communication skills, with the ability to translate complex technical results into clear business recommendations.

Preferred

Experience in the financial services, fintech, or online payments industry, particularly in risk, credit, or fraud domains.
Knowledge of AWS, payment rule systems, and credit products is a plus.
Familiarity with experimental design