Experience Level: Mid-Senior
Experience Required: Up to 2 years
Education: Bachelor’s degree
Job Function: Finance / Risk Analytics
Industry: Financial Services
Employment Type: Contract (covering multiple leaves over ~1 year; potential to extend based on business needs and performance)
Work Setup: Hybrid — must be based in the San Jose area
Visa Sponsorship: Not available
Relocation Assistance: No
Schedule: Mon–Fri, Day Shift (Pacific Time)
Overview
We’re looking for a talented, detail-oriented analyst to support the Fraud Risk Strategy team. You’ll contribute to projects in fraud detection, risk analysis, and loss mitigation , using statistics and data science to solve real-world challenges in digital payments and eCommerce. This hands-on role involves high collaboration with cross-functional teams and significant business impact.
Key Responsibilities
Design and refine rules to detect and mitigate fraud across customer segments
Develop Python scripts and models that enhance fraud detection and automation
Investigate complex or high-impact fraud cases and identify root causes
Define and execute strategies for multiple risk types
Collaborate with Product and Engineering teams to improve control systems
Build dashboards and visualizations (Tableau or AWS QuickSight) to track KPIs
Present insights and recommendations to stakeholders and leadership
Must-Have Qualifications
Up to 2 years in risk analytics, data analysis, or data science within eCommerce, online payments, or user trust/fraud domains
Bachelor’s degree in Data Analytics, Data Science, Mathematics, Statistics, or related field (or equivalent experience)
Proficiency in SQL, Python, and Excel , including key data science libraries
Experience working with large datasets
Skilled in data visualization using Tableau (AWS QuickSight a plus)
Strong analytical and communication skills, with the ability to explain results to technical and non-technical teams
Nice to Have
Experience solving risk or fraud problems using analytics
Familiarity with AWS , payment rule systems , and machine learning workflows
Understanding of fraud investigations and typologies
Expected Outcomes (6–12 Months)
Design and implement data-driven fraud strategies to reduce loss and improve customer experience
Develop dashboards to monitor key fraud metrics and performance indicators
Partner with teams to deploy scalable, real-time fraud detection solutions
Deliver actionable insights and recommendations that influence business decisions
Interview Process
Two to three Zoom interviews
SQL skills assessment during the first interview
Contract position covering multiple leaves (approx. 12 months)