• Design and evaluate strategies to reduce fraud and abuse across promotions, referrals, refunds, and payment flows. • Develop and improve machine learning models, rules-based systems, and heuristics to detect high-risk behavior while minimizing false positives. • Analyze experimental and observational data using A/B testing and causal inference to measure the effectiveness of interventions in payments and fraud prevention. • Identify and quantify key friction points in the payments funnel (e.g., latency, failure rates, PSP routing), and partner with Engineering to improve reliability and speed. • Build dashboards, reports, and self-serve tools to empower teams with visibility into fraud trends, payment performance, uncollected revenue, and risk tradeoffs. • Collaborate on incident response and root cause analyses for fraud and abuse events, ensuring rapid mitigation and long-term prevention. • Stay informed on evolving fraud tactics, risk mitigation techniques, and payment technologies to proactively shape Lime’s strategy.

• Analyze product data to inform product direction • Collaborate with engineering and product teams • Conduct foundational analyses on unsolved questions • Share complex analyses that inform decision-making

• Lead, mentor, and develop a data team (data analysts and engineers) • Define and execute the data strategy aligned with business goals • Ensure data quality, reliability, and governance across the organization • Build and maintain scalable data models and data architecture • Drive data democratization, enabling non-technical stakeholders to access and use data • Partner with Product and Marketing teams to identify opportunities and improve performance (e.g. acquisition, retention, user behavior) • Optimize and evolve the current data stack with a business-impact mindset

• Research new techniques and frameworks for NLP tasks • Design new experiments for data abstraction and categorization of sentences/paragraphs • Work with our lawyers to review initial results and implement pre and post-processing scripts to improve the accuracy of models • Run data analysis on our dataset to design potential rules for annotation • Improve the architecture of data modeling and data tooling in partnership with Engineering and cross-functional business partners • Own your projects and use this autonomy to find creative and innovative ways of solving problems and delivering solutions.