Data analysis
Anti-Fraud Data Analyst - USDS
Location
:
San Jose
Employment Type
:
Regular
Job Code
:
A02965A
Responsibilities

MINIMUM QUALIFICATIONS:

• Bachelor’s degree
• Proficient in SQL and familiar with Python or R for data analysis
• 1+ years of relevant experience in one of the following areas: Anti-fraud, Financial Services, Risk Management, Trust & Safety, Data Analytics, Data Science in eCommerce or Tech industry in general;
• Excellent communication with the ability to work with global teams with little supervision
• Intellectually curious, data-driven, result-oriented, and has a high desire to learn and contribute
• Ability to navigate ambiguity and show good judgement
• Willingness to travel 10-20% if needed

PREFERRED QUALIFICATIONS:

• Bachelor’s degree in mathematics, statistics, computer science, engineering is preferred
  Job Information
  【For Pay Transparency】Compensation Description (Annually)
  The base salary range for this position in the selected city is $106400 - $232223 annually.
  ​
  Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
  ​
  Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
  ​
  The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
  ​

• Manage end-to-end, large-scale quantitative analysis projects and monitoring systems
• Apply investigational and analytical skills to monitor, audit, and analyze potential fraudulent cases and develop anti-fraud rules
• Design processes and build effective workflows to define characteristics of different fraud types, capture new risk types, and collaborate with cross-functional teams to drive improvements of existing tools, processes, and models related to fraud management
• Understand the trends and telemetry of fraudulent activities in each market and design effective monitoring tools to mitigate fraud
• Define key performance metrics and build detailed reporting models for effective tracking, reporting, and feedback on measures implemented
• Research fraud-related policies and build effective enforcement processes to prevent and penalize bad actors while balancing user experiences
• Communicate findings and complex results to key stakeholders and manage timelines of key projects
• Collaborate with cross-functional teams to come up with data-driven solutions
• Provide data-driven guidance on fraud policies and enforcement measures to influence key stakeholders
  Qualifications