Chief Supply Chain Optimisation Strategist

1 week ago

Singapore beBeeLeadership Full time

Senior Fulfillment Optimization Leader

Our fulfillment team is a key part of Grab's infrastructure, enabling seamless coordination between diverse marketplaces across Southeast Asia. We design real-time, distributed systems and machine learning solutions to process hundreds of millions of requests per day, driving efficient supply allocation, pricing, and order matching.

Our mission: Deliver best-in-class products for our driver-partners. Maximise efficiency in fulfilling consumer demand - rain or shine. Create sustainable, efficient marketplaces that balance experience and cost for all stakeholders.

We're seeking a Senior Principal Machine Learning Engineer to join our Fulfilment team and take the lead in Fulfilment strategy optimization and driver behaviour modelling - a strategic area for understanding our partners and optimising our pricing, dispatch and supply management policies.

What You Will Do:

  • Optimise cross-system fulfilment strategies using advanced ML/DL models
  • Develop unified RL architectures to coordinate multiple levers (pricing, dispatching, and supply planning) with differing objectives and time scales
  • Build multi-agent or hierarchical RL frameworks to jointly optimise pricing, dispatching, and repositioning decisions, pushing the marketplace Pareto frontier
  • Research scalable representations of marketplace state that incorporate supply-demand signals, elasticity, traffic, weather, and driver intent
  • Develop robust, interpretable models to capture driver decision-making under varying operational conditions
  • Design feedback loops that adapt to driver behaviour over time and across geographies
  • Collaborate with platform and experimentation teams to run real-world validations and iterate on model design
  • Build tooling and simulations to support counterfactual analysis and platform design decisions
  • Operate as a technical lead, guiding data scientists in these efforts while fostering a collaborative and high-performance environment
  • Work with data engineers and backend engineers to integrate optimisation models into real-time production systems

What You Need To Succeed:

  • Master's degree in Computer Science, Operations Research, Applied Mathematics, or related field with at least 10 years of relevant experience
  • Experience with Reinforcement Learning, MDPs, stochastic control, or behavioural modelling under uncertainty
  • Experience developing and deploying ML models that incorporate online learning, temporal decision processes, or simulation-based optimisation
  • Fluent in Python and ML frameworks (e.g., PyTorch, TensorFlow)
  • Familiar with distributed computing systems or scalable training platforms (e.g., Spark, Ray)
  • Able to translate high-level business problems into tractable modelling tasks

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