Fraud Risk Data Scientist

About the role

As a Fraud Risk Data Scientist in the Fraud Risk team, you will play a key role in using advanced analytics and data-driven insights to identify, assess, and mitigate fraud risks across our financial products. The primary focus of this role is to develop robust data driven risk strategies that enhance fraud detection and prevention capabilities across various product lines, such as Payroll, Bill Pay, and Card Transactions. Risk machine learning model experience is a plus. Your responsibilities will include:

What you will do

• Develop data-driven fraud detection strategies: Use advanced analytics to design and enhance fraud detection strategies that address risk in real-time across multiple financial products, including transaction monitoring, onboarding, and account takeover (ATO) detection.
• Analyze fraud patterns and risk trends: Perform deep analysis on transactional data to identify fraud patterns, emerging threats, and vulnerabilities, and translate these findings into actionable risk mitigation strategies.
• Collaborate across teams: Work closely with Fraud Risk Strategy, Security, Product, and Engineering teams to align fraud prevention initiatives with business goals, ensuring analytics-driven decisions are integrated into product development and operational workflows.
• Conduct comprehensive cost-benefit analyses: Evaluate the trade-offs between fraud risk reduction, customer experience, and operational efficiency to develop optimal fraud prevention strategies.
• Enhance ATO detection through data analysis: Use data analytics to identify suspicious behaviors and enhance ATO detection, working closely with the security team to define rules, analyze data, and measure effectiveness.
• Monitor and refine risk strategies: Continuously assess and improve fraud detection strategies based on new data insights, fraud trends, and ongoing performance evaluations to stay ahead of emerging fraud tactics.
• Collaborate across teams, including Security, Sales, Customer Service, and Compliance, to enhance fraud monitoring and response capabilities.
• Partner with R&D to design, build, and maintain tools for fraud detection and investigation, ensuring they meet evolving business needs.

What you will need

• 1+ year of experience in data science and analytics: Demonstrated experience in using analytics and data science techniques to solve fraud-related challenges, particularly in financial technology, payments, or SaaS industries.
• Expertise in data analysis: Proficient in extracting insights from large datasets, with hands-on experience using tools such as Python, R, SQL, and other data analysis platforms to create robust fraud detection strategies.
• Data-driven approach to decision-making: Experience in developing data-driven strategies that address fraud risks while balancing the impact on customer experience and operational efficiency.
• Effective cross-functional collaboration: Proven ability to collaborate with product, risk, security, and engineering teams to drive fraud risk initiatives.
• Educational background: Bachelor's degree in a relevant field such as Data Science, Mathematics, Statistics, or Operations Research. A Master’s degree is preferred.

Nice to Have

• Experience with machine learning models: Familiarity with building and deploying machine learning models for fraud detection.
• Experience in SaaS or FinTech environments: Prior experience working in a fast-paced, tech-driven environment with a focus on financial services or SaaS is beneficial.

Additional Information

This role will receive a competitive salary + benefits + equity. The salary for US-based employees will be aligned with one of the ranges below based on location; see which tier applies to your location **here.**

A variety of factors are considered when determining someone’s compensation–including a candidate’s professional background, experience, and location. Final offer amounts may vary from the amounts listed below.

The pay range for this role is:

124,000 - 155,000 USD per year (US Tier 1)