Data Analytics Manager

Join Remitly, a leading digital remittance company, as a Data Analytics Manager in the Fraud department. You will lead and develop a team of analysts while remaining hands-on in data analysis. Your role will involve applying data science and analytics techniques to drive decision-making across Remitly's Transaction Integrity program, which includes Fraud, Scams, and Promo Abuse. You will work closely with program managers, product managers, engineers, and operations to protect customers and enable sustainable growth.

• Lead and develop a team of analysts while remaining hands-on in the data, providing mentorship, technical guidance, and career development support.
• Apply data science and analytics techniques to drive decision-making across Remitly's Transaction Integrity program, leveraging AI to accelerate analytical work.
• Own and evolve the metrics used to evaluate fraud system performance, operational effectiveness, and customer friction, and formulate hypotheses to translate analytical findings into actionable recommendations.

• Bachelor's degree in mathematics, engineering, economics, or another quantitative field (or equivalent additional experience)
• 5+ years of experience in a data analyst role or similar full-time professional experience, with at least 2–3 years of people management experience
• Proven ability to coach and develop analysts, elevate team output through technical guidance, and navigate ambiguous problem spaces
• Experience distilling business problems from stakeholder discussions, developing hypotheses, building an analytical framework, carrying out analyses, and writing clear recommendations
• Experience in fraud, risk, trust & safety, fintech, or adversarial analytics strongly preferred
• Expertise with statistical concepts (e.g., A/B testing, probability theory, forecasting) and how they apply in real-world fraud or risk contexts
• Strong SQL proficiency; working knowledge of Python or R for data analysis
• Strong data storytelling skills, including data visualization, technical writing, and the ability to communicate complex findings to non-technical audiences
• Demonstrated experience using AI tools (e.g., LLM-assisted analysis, agentic workflows, or AI-powered tooling) to enhance analytical productivity and quality