Lead Data Scientist (Mobility)

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

You'll join the Mobility Data Science team at the core of Grab's founding business. We build high-concurrency AI models that power the ride-hailing experience for millions of passengers across Southeast Asia. Our team balances a complex two-sided marketplace through geospatial modeling, behavioral economics, and real-time optimization algorithms. We work with Engineering, Product, and regional operations teams to translate marketplace challenges into scalable data solutions.

Get to Know the Role

As a Lead Data Scientist (Mobility), you'll report into the Senior Data Science Manager and be based onsite at One North Grab Singapore office. You'll serve as a senior technical contributor within the Mobility DS team, owning complex AI projects from problem definition through to production deployment. You'll architect scalable data science solutions that power our core ride-hailing engine, make decisions on ML system design, and guide the technical implementation of models that handle millions of daily transactions. You'll also provide hands-on technical guidance to junior team members through code reviews and architectural mentoring.

The Critical Tasks You will Perform

You'll:

• Design, build, and deploy production-grade AI models for ride-hailing services, managing the full lifecycle from prototyping through A/B testing frameworks to production monitoring systems.
• Develop generative AI experiences for ride-hailing, building systems that predict passenger intent, handle multi-modal transit queries, and manage service disruptions through agentic, conversational interfaces.
• Build AI-driven targeting models to identify under-served user segments and decode commuting behaviours, powering automated promotions and user acquisition strategies across spatial grids and time horizons.
• Implement optimization algorithms that balance demand and supply in real-time, using contextual nudges and spatial-temporal adjustments to maximise network throughput and reduce unfulfilled trips.
• Develop predictive models to identify high-risk bookings and cancellation probabilities before rides begin, automating fault attribution logic to ensure fair outcomes for passengers and drivers.
• Review Python code and ML methodologies from junior data scientists, identifying architectural improvements and establishing engineering standards for model quality and scalability.

Qualifications

What Essential Skills You will Need

• Production ML Engineering: You've deployed models using TensorFlow or PyTorch in live production environments, with demonstrated experience managing model versioning, automated testing, and performance monitoring systems.
• Generative AI Development: You've fine-tuned LLMs and built agentic applications using frameworks such as LangChain or similar tools, specifically for commerce or service-based workflows.
• Spatio-Temporal Modelling: You've built geospatial models and time-series forecasting systems for demand prediction across tight spatial grids, using this to drive targeting or resource allocation decisions.
• Optimization Algorithms: You've implemented reinforcement learning or combinatorial optimization solutions for real-time marketplace balancing or resource allocation problems.
• Predictive Risk Modelling: You've developed classification models to predict event probabilities (such as cancellations or service failures) and automated decision systems based on model outputs.
• Technical Code Review: You've reviewed machine learning code and system architectures for other data scientists, providing specific feedback on model efficiency, scalability, and methodological soundness.
• Cross-Functional Technical Translation: You've worked with product managers to translate business requirements into technical specifications, and partnered with software engineers to integrate ML models into production codebases.

Additional Information

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For at Grab

We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.