Fraud Data Analyst

Remitly•Seattle, WA

2d•$129,709 - $145,000•Hybrid

About The Position

Under the supervision of the Director, Analytics, the Fraud Data Analyst will apply advanced data analytics and statistical methodologies to detect fraud risks, evaluate product performance, and drive data-informed strategies that enhance compliance, customer experience, and long-term product integrity. Telecommuting is available up to 3 days per week within the Seattle Metropolitan area, based on manager approval.

Requirements

Master’s degree in Data Analytics, Data Engineering, Computer Science, Engineering, or a related field
2 years of experience with data analysis, visualization, and reporting
2 years of experience with analyzing large volumes of structured and unstructured data
2 years of experience with applying Root Cause Analysis (RCA) to identify and resolve issues in fraud detection, governance, and product workflows
2 years of experience with analyzing clickstream and behavioral data
2 years of experience with conducting A/B testing and statistical analyses
2 years of experience with evaluating machine learning models, including precision/recall, AUC, holdout analysis, and supporting data science teams in monitoring production models
2 years of experience with building robust data models and transforming raw data into actionable insights
2 years of experience with designing and managing ETL workflows and data pipelines
2 years of experience with gathering requirements from stakeholders, managing tasks, and delivering end-to-end fraud analytics solutions to internal customers
2 years of experience with collaborating cross-functionally with Product, Engineering, and Operations teams
2 years of experience with translating complex quantitative analyses into actionable recommendations for technical and non-technical stakeholders
2 years of experience with utilizing BigQuery, MS SQL Server, MySQL, PostgreSQL, Power BI, Python, and Tableau

Responsibilities

Gather, analyze, manipulate, and process large data sets using statistical software and advanced data analytics technologies to assess fraud risks, balance risk and reward in product development, evaluate product system performance, and enhance operational effectiveness.
Design and execute fraud-focused product analytics experiments, leveraging A/B testing, computer science techniques, and product performance analyses to identify fraud patterns and drive product enhancements.
Collaborate with Product, Business Leaders, and Compliance teams to implement fraud-related data insights into new product experiences, ensuring compliance and continuously improving customer experience through data-driven recommendations.
Leverage findings from fraud analytics to define and execute long-term fraud prevention strategies and product roadmaps across cross-functional product teams, aligning risk management with business objectives.