Senior Data Scientist (Analytics) - GFin at Grab

Get to Know the Team The GFin Analytics team serves as the intelligence engine driving data-informed decisions across our payments and financial services products. As part of the Payments Tech Family, we're instrumental in accelerating cashless payments across all Grab verticals. We're looking for analytically rigorous data analysts who have embraced AI as a force multiplier. If you excel at experimentation and behavioral analysis while leveraging LLMs to work smarter, and if you think in terms of scalable solutions rather than one-off analyses, we want to talk to you. Get to Know the Role We're looking for a Senior Data Scientist (Analytics) to lead critical analytical projects for the GFin Analytics team at Grab. Our mission centers on understanding user experiences at a granular level and collaborating with Business, Product, Design, and Engineering to deliver the right outcomes and features. You will directly report to the manager. You will be based onsite. The Critical Tasks You Will Perform • Design, measure, and recommend A/B tests and multivariate experiments across the platform. These experiments evaluate layout optimization, UI/UX efficacy, contextualization, algorithm performance, and API effectiveness. Additionally, use LLMs to accelerate experiment design, automate statistical analysis, and create insight narratives. • Extract insights from clickstream and transactional data to understand user behavior across consumers, driver partners, and merchants, translating patterns into relevant product metrics, using AI coding assistants to write and improve complex SQL queries. • Create segmented customer and merchant views that allow business and product team to drive meaningful product iterations, building AI assisted segmentation frameworks that automatically identify emerging patterns and anomalies. • Transform ambiguous business challenges into structured analytical frameworks and conduct deep dive analyses that yield clear insights, using LLMs to rapidly prototype analytical approaches and synthesize findings into compelling narratives. • Balance analytical rigor with technical complexity and explainability while considering real world business constraints, leveraging AI to generate multiple solution approaches and evaluate trade offs efficiently. • Collaborate with stakeholders to define key metrics, align on measurement strategies, and design robust experimentation approaches, using AI to draft documentation, create presentations, and automate routine reporting. • Develop data-driven solutions, including reports, dashboards, models, and frameworks, that directly support stakeholder decision making. These solutions involve building AI-powered self-serve analytics tools that scale automatically and systematically.