Senior Risk Analyst

As the Sr. Risk Analyst, you will join a team of talented and dedicated fraud experts who play a pivotal role in fighting fraud and helping our customers succeed. Your work will make a big impact in advancing payment risk decisioning to the next level. The right candidate is an experienced fraud fighter with a love for learning, problem solving, and not afraid of a challenge. They enjoy being part of a close-knit team and working on multiple projects simultaneously without compromising quality.

Primary Responsibilities Include:

1. Perform proactive risk analysis to identify risk factors and trends of potentially fraudulent activity on large datasets.
2. Identify fraud trends, document and communicate findings, and lead in the development and implementation of new fraud prevention strategies.
3. Support Risk Operations with process and tool improvements.
4. Assist our sales team by leading fraud and tool insights conversations with potential merchants.
5. Gather and format data into regular and ad-hoc reports and presentations as well as dashboards for metrics and trend reporting.
6. Assist in special projects and ad-hoc assignments, upon request.
7. Develop and maintain strong relationships with internal teams and external business partners.

Required Skills/Traits:

1. Bilingual with business proficiency in English.
2. 5+ years experience in e-commerce, payments or risk-related industry with either traditional and non-traditional payment methods.
3. Strong understanding of ecommerce payments landscape in LATAM.
4. Strong analytical skills and an understanding of how to utilize data to target improvements and solve problems.
5. Experience with database language (e.g., SQL) as well as BI and reporting tools (Looker, Tableau, Splunk, etc.).
6. Strong critical thinking skills with advanced judgment capability.
7. Ability to manage multiple assignments while working independently.
8. Strong communication skills with internal and external stakeholders.
9. Flexibility to work weekends and holidays.
10. Experience in large scale data analysis, tools, and processes (e.g., Python, R).
11. Experience with translating fraud trends into rule logic and/or feature suggestions.