Fraud Risk Data Scientist

Job Description

Job Title: Data Scientist – Fraud Risk AI

Location: Hybrid (San Jose, CA preferred) Remote considered for exceptional candidates

Employment Type: Contract (1 Year) Potential for Extension

Work Schedule: Monday to Friday, Pacific TimeAbout the Role

We are seeking a talented, driven, and detail-oriented Data Scientist to join our Fraud Risk Data Science team within the Risk Data AI Innovation organization. In this role, you will play a key part in supporting critical initiatives focused on fraud detection, risk analysis, and loss mitigation. The ideal candidate will bring a strong background in machine learning, data science, and statistical modeling—especially as applied to fraud or trust/risk use cases.

You will contribute to designing and deploying scalable AI/ML solutions, monitoring performance of fraud models, and partnering with cross-functional teams to implement real-time risk solutions. Experience with large language models (LLMs) or similar AI tools is highly desirable.Key Responsibilities

Design, develop, and implement machine learning and AI models to detect and mitigate fraud.

Collaborate with stakeholders, engineers, and product managers to deploy scalable, real-time AI solutions.

Analyze large datasets to identify patterns, trends, and opportunities for improving fraud prevention strategies.

Monitor model performance and create dashboards/visualizations to track KPIs and ensure continuous improvement.

Support the development and integration of LLMs and other generative AI tools for risk-related use cases.

Translate complex analytical findings into clear, actionable insights for technical and non-technical audiences.

Drive AI transformation across fraud risk management initiatives at BILL.Required Qualifications

2–6 years of hands-on experience in machine learning, AI, data science, or risk analytics.

Relevant industry experience in eCommerce, online payments, user trust/risk/fraud, or product abuse investigations.

Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related field—or equivalent practical experience.

Demonstrated ability to solve complex business problems using statistical modeling and machine learning techniques.

Strong proficiency in SQL, Python, AWS, Excel, and core data science libraries.

Experience with data visualization tools, particularly Tableau or AWS QuickSight.

Proven success working with large-scale datasets.

Excellent communication skills, including the ability to explain technical concepts to diverse stakeholders.

Comfortable navigating ambiguity and translating it into clear business objectives and testable hypotheses.Preferred Qualifications

Experience developing fraud detection or risk mitigation models.

Exposure to or experience implementing LLMs or AI tools in a risk-related context.

Familiarity with model monitoring best practices and performance tracking.

Strong analytical thinking with a data-driven mindset.

Project management experience in cross-functional environments.Key Deliverables

End-to-end design and deployment of fraud detection and mitigation models.

Insightful dashboards and performance visualizations to measure model effectiveness.

Collaboration with engineering and product teams to operationalize data science solutions.

Clear and compelling presentations of findings and recommendations to leadership.

Ongoing monitoring and refinement of deployed models to adapt to evolving fraud patterns.Additional Information

Hiring Manager Notes:

Must have strong SQL skills.

High value placed on experience solving business challenges using data science in fraud mitigation.

Familiarity with AWS Quicksight and Tableau is a plus.

Interview Process:

2–3 rounds of Zoom interviews.

SQL skills will be assessed during the first interview.

Work Type: Contract to cover multiple leaves over a 12-month period.

Location: Hybrid role in San Jose, CA. Remote candidates will be considered if no viable local options are available.

Full-time

Hybrid remote