Data Analyst, Fraud

Remitly

Remitly aims to redefine financial services globally. Since its establishment in 2011, the company has committed to providing trusted financial solutions for individuals transferring money internationally. The Data Analyst, Fraud role exists to enhance Remitly’s ability to combat fraud and improve customer experience, with a focus on tailoring solutions regionally. The role is integral to Remitly’s mission of expanding financial services beyond traditional boundaries.

In this role you can expect to have the responsibilities:

• Develop expertise in Remitly’s fraud-related data and policies.
• Construct coherent interpretations of data, customer experiences, and objectives.
• Establish metrics balancing risk, system performance, and operational efficiency.
• Collaborate with Business Management to improve customer experiences and achieve business objectives.
• Conduct experiment design, A/B testing, and ad hoc analysis.
• Form hypotheses, design product experiments, and evaluate outcomes.
• Provide insights to shape long-term product strategies and roadmaps.

This role comes with the following benefits:

• Paid Vacation Days
• Health insurance
• Commuter benefits
• Employee Stock Purchase Plan (ESPP)
• Mental Health & Family Forming Benefits
• Continuing education and travel benefits

This role requires you to have:

• 2+ years of experience in an analyst role or similar in risk, fraud, or fintech.
• Bachelor’s degree in a quantitative field or equivalent experience.
• Proficiency in SQL for data analysis.
• Foundational knowledge of statistics.
• Strong data communication skills, including visualisation and technical/business writing.
• Ability to derive creative solutions within complex systems.

You would benefit from having:

• Experience in adversarial analysis such as risk or fraud.
• Knowledge of Python or R for statistical analysis and modeling.

Remitly emphasizes a nondiscriminatory environment, celebrating diversity, and providing equal employment opportunities based on qualifications without bias regarding personal attributes or statuses.