Lead data-driven product strategy initiatives supporting fraud detection and prevention systems
Design and deliver insights, reporting, and analytical frameworks to detect and mitigate fraud at scale
Define, track, and evaluate key metrics, including machine learning model performance and business impact
Partner with cross-functional teams to influence experimentation design, causal inference, and measurement strategy
Build intuitive dashboards and data visualizations to communicate insights to technical and non-technical stakeholders
Develop compelling data narratives that inform product, policy, and operational decisions
Drive structured decision-making through reviews, scorecards, OKRs, and planning processes
Lead coordination of high-visibility initiatives, including running standups, aligning stakeholders, and tracking progress
Define project scope, objectives, timelines, and resource requirements for complex programs
Develop and manage detailed project plans, proactively identifying and mitigating risks

5+ years of experience in analytics, data science, or a related field
Advanced degree in a quantitative field (e.g., Statistics, Computer Science, Economics, Mathematics) preferred
Expert-level proficiency in SQL and Python
Strong experience in experimentation design , including A/B testing, power analysis, and causal inference
Solid understanding of machine learning models and performance evaluation
Experience working with large-scale datasets, ETL pipelines, and data modeling frameworks
Ability to operate effectively in ambiguous environments and drive solutions end-to-end
Strong communication skills with the ability to translate complex data into actionable insights
Experience in Trust & Safety, fraud detection, or risk domains is a plus

Advanced Analyst – Data Science, Trust & Safety

Key skills