Lead Data Scientist (Analytics) - Digital Marketing

About Grab and Our Workplace Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility. Get to Know the Team The Growth Data Science team is a key component of our regional growth strategy. We focus on creating and implementing innovative approaches to drive growth through performance marketing automation, machine learning models, geospatial insights, user lifecycle and funnel optimisation, behavioural forecasting, and other advanced analytics methods. Our work spans across transportation, food, fintech, logistics, and platform services. Get to Know the Role We are seeking a experienced and versatile Manager, Growth Data Science - Digital Marketing with deep expertise in paid marketing measurement, AI-driven analytics, and operations to join our growing team. In this role, you'll collaborate with marketing leaders, analytics teams, and cross-functional stakeholders to solve complex digital marketing challenges using advanced measurement science, artificial intelligence, and data-driven approaches. You'll work on end-to-end paid marketing analytics initiatives, with opportunities to design and implement sophisticated attribution models, incrementality studies, media mix modeling (MMM), causal inference methodologies, and AI-powered tools that enhance reporting, measurement, and experimentation capabilities. You will directly report to the Senior Manager, you will be based working onsite. The Critical Tasks You Will Perform Paid Marketing Measurement & Analytics • Partner with marketing leaders and stakeholders to understand business objectives, marketing channels, data sources, measurement constraints, and strategic priorities. • Translate marketing needs into relevant measurement science solutions, evaluating multiple methodological approaches and communicating trade-offs between attribution models, incrementality testing, and causal inference methods. • Design and implement advanced attribution methodologies (multi-touch, algorithmic, and rule-based models) to accurately measure campaign contribution across paid channels and customer touchpoints. • Develop and execute incrementality measurement studies to isolate true campaign impact, including A/B testing, holdout analysis, and matched market approaches for paid marketing campaigns. • Build and maintain Media Mix Modeling (MMM) frameworks to quantify the impact of paid marketing spend across channels, optimize budget allocation, and forecast campaign performance. • Design and conduct geo-lift studies to measure causal impact of paid marketing initiatives at regional or market levels, supporting strategic decision-making and ROI validation. • Conduct rigorous A/B tests and multivariate tests to improve paid campaign performance, measure incremental lift, and increase conversion rates and customer acquisition efficiency. • Collaborate with stakeholders to align on measurement methodology, success metrics, deliverables, and project roadmaps for all paid marketing analytics projects. AI & Automation for Marketing Intelligence • Leverage advanced AI and machine learning tools to automate reporting workflows, generate real-time marketing insights, and accelerate decision-making across paid marketing channels. • Design and build AI-powered measurement and attribution tools that enhance the speed and accuracy of campaign performance analysis, reducing manual effort and improving stakeholder accessibility to insights. • Implement machine learning models and algorithms to optimize campaign targeting, budget allocation, and bid strategies, translating AI predictions into actionable marketing recommendations. • Develop AI-driven experimentation frameworks that automate test design, statistical analysis, and result interpretation, enabling faster iteration and more sophisticated measurement of marketing impact. Data Engineering & Operations • Develop and manage detailed project plans including milestones, risks, owners, and contingency plans for measurement science projects. • Create and maintain efficient data pipelines using SQL, Spark, and cloud-based big data technologies. These pipelines ingest, process, and integrate paid marketing data from ad platforms, conversion tracking systems, and internal data sources. • Collect, clean, and integrate large datasets from multiple marketing channels (paid search, social, display, video) and attribution platforms to support measurement requirements. • Build analytics tools and dashboards that deliver applicable insights on campaign performance, channel effectiveness, customer acquisition costs, and marketing ROI. • Perform exploratory data analysis, statistical modeling, and causal inference analysis to uncover insights and inform strategic marketing decisions. • Train, validate, and tune measurement models using modern statistical and machine learning techniques, ensuring model accuracy and business applicability. • Document measurement methodologies, model results, and findings in clear, team member-ready formats and support implementation of insights within marketing operations. Stakeholder Collaboration & Leadership • Lead cross-functional collaboration between marketing, analytics, product, and data engineering teams to ensure measurement frameworks align with business objectives. • Communicate complex measurement science concepts, AI capabilities, and statistical findings to non-technical stakeholders, translating results into applicable marketing recommendations. • Mentor junior analysts and data scientists on measurement methodologies, AI-driven analytics best practices, paid marketing analytics,…