Fraud Analytics Analyst CC/CL

Analyze large datasets to identify trends, patterns, and indicators of fraud, focusing on both broad fraud trends and specific areas such as credit card and transactional fraud.

Develop and refine fraud detection models and algorithms to improve detection rates and false positives.

Drive research projects through all its phases, from inception to deployment and impact assessment.

Prepare and present analytical reports on fraud trends, findings, and the effectiveness of fraud prevention strategies.

Write queries, design, build and maintain dashboards and visualizations to monitor randomized experiments and key metrics.

Continuously quantify the impact in gross margin of fraud initiatives, size the economic opportunity of product vulnerabilities and process inefficiencies.

Work cross functionally with stakeholders in Data Science, Fraud Operations, Product, and Go-to-market.

Bachelor’s degree in applied quantitative field, such as statistics, economics, mathematics, etc.

1-2 years of work experience with transactional fraud in credit lending and credit cards, working in the telecom industry and a fast-paced and rapidly changing environments.

Strong SQL competencies, and experience with statistical programming languages such as R or Python.

Excellent written and oral communication skills, summarize and communicate technical analysis to broad audiences.

Strong analytical skills and who are curious by nature. 

Results-oriented and feels comfortable working both autonomously and in heavily cross-functional environments, taking full ownership of his/her own projects.