Lead Data Scientist (Grabmart) at Grab

Get to know the role: We are looking for Lead Data Scientists to own high-impact projects end-to-end—from scoping the problem and proposing scalable DS architectures to deploying services and driving continuous model evolution. You will operate with high autonomy, partnering with Engineering, Product, and Analytics teams to deliver advanced data-driven solutions. Core Responsibilities: • Extract Insights: Analyze massive datasets (transactional, behavioral, and geospatial) to decode shopping patterns and optimize the end-to-end journey for grocery and retail services. • Identify Opportunities & Propose AI Solutions: • Next-Gen User Experience: Move beyond static interfaces to build agentic, conversational, and anticipatory experiences. You will predict shopping intent and brand preferences to power autonomous shopping assistants that can handle complex queries, automate re-ordering, and provide hyper-personalized discovery. • Demand Forecasting: Create high-precision, hyper-local spatio-temporal models using complex signals from different sources. You will provide granular SKU-level predictions at various time scales to optimize supply chain readiness, inventory pre-positioning, and resource allocation to minimize stock-outs and waste. • Demand Shaping: Implement intelligent optimization algorithms to balance demand and supply in real-time. You will optimize platform visibility and leverage contextual nudges spatially and temporally to balance demand with merchant/driver supply, maximizing network throughput and reducing unfulfilled orders. • Demand Generation: Identifying "hidden" customers and decoding their unique shopping patterns to unlock new demand. You will build AI models to power smart, automated targeting of diverse user segments to drive sustainable growth. • Service Reliability: Build predictive models to safeguard the order lifecycle. This includes identifying high-risk orders prone to cancellation, predicting fulfillment failures due to insufficient inventory, and flagging transactions that require special handling or proactive intervention to ensure a perfect delivery experience. • Build, Deploy and Iterate: Develop production-grade AI models. Refine performance through rigorous A/B testing and real-time monitoring. • Collaborate Cross-Functionally: Work alongside PMs, Engineers, and Operations to scale solutions across the region. • ML Operational Excellence: Drive for ML operational excellence in data science projects by ensuring robust versioning, automated testing, seamless deployment, performance monitoring, and continuous improvement of ML pipelines. • Innovate: Drive the team's technical roadmap and contribute to IP creation and research.