Senior Principal Data Scientist

Get to Know the Team

The Fulfilment Tech Family is an integral part of Grab, connecting our wide-ranging marketplaces across Southeast Asia. We develop real-time distributed systems and machine learning solutions to process hundreds of millions of requests daily, ensuring efficient allocation of supply, pricing, and seamless order matching.

Our mission:

• Build reliable and impactful products for driver-partners.

• Optimize the ability to fulfil consumer needs in any conditions.

• Develop sustainable marketplaces, balancing user experience and operational costs across stakeholders.

We're hiring a Senior Principal Data Scientist to join the Fulfilment team. You're an individual contributor whom will lead strategic optimization initiatives that elevate pricing, dispatch, and supply management strategies. You'll combine in-depth data-driven methodologies with a strategic mindset to drive automation and marketplace efficiency.

Get to Know the Role

As a Senior Principal Data Scientist, you'll shape and advance cross-system Fulfilment strategies, accounting for how passengers and driver-partners respond to platform interventions. You'll leverage machine learning and operations research methodologies to optimize marketplace-level operational outcomes.

Through joint optimization across systems-pricing, dispatch, and supply positioning-you'll design interpretable, adaptable, and multi-objective-aware Reinforcement Learning decision agents. You'll model marketplace behaviour and develop policies to automate key aspects of platform operations, contributing lasting impact for customers, driver-partners, and Grab.

You'll report to the Head of Data Science and work onsite at Grab's One North Singapore office.

The Critical Tasks You Will Perform

You will:

• Develop ML and DL models to improve generalisation and operational efficiency
• Create unified Reinforcement Learning architectures that coordinate multiple elements, including pricing, dispatch, and supply positioning, while balancing diverse marketplace objectives.
• Design frameworks for joint optimization of marketplace systems through hierarchical or multi-agent RL approaches that push the operational Pareto frontier.
• Explore representations of data that consider supply-demand signals, behaviour elasticity, and factors like weather, traffic, and user intent.
• Build interpretable models to capture passenger and driver decision-making across diverse scenarios and operational conditions.
• Devise feedback loops to respond to passenger and driver behaviour, considering geographical differences and behavioural patterns over time.
• Collaborate with data engineers and backend teams to refine and integrate optimization models into real-time production systems.
• Work with the platform and experimentation teams to validate models in live environments and drive iterative improvements.
• Guide technical teams by leading efforts in data science while fostering collaboration and excellence.
  

What Essential Skills You Will Need

• You have a Master Degree in Computer Science, Operations Research, Applied Mathematics, or related field with at least 10 years of relevant experience.

• You have experience with Reinforcement Learning, MDPs, stochastic control, or behavioural modelling under uncertainty.

• You have experience deploying ML models in online learning systems, sequential decision frameworks, or simulation-based techniques for optimization tasks.

• You have fluency in Python and experience with ML libraries such as PyTorch or TensorFlow for developing robust learning algorithms.

• You are familiar with tools such as Spark or Ray to build scalable workflows for data-driven optimization and training.

• You can bridge business challenges into concrete modelling problems with actionable outputs.

• You have advanced skills to interpret platform data and understand marketplace elasticity, user behaviour, and external environmental influences, contributing to actionable insights for real-world applications.

  You may also apply directly via https://smrtr.io/wRtCm