Data Scientist

GRABTAXI HOLDINGS PTE. LTD.; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading tech company in Singapore is seeking a Senior Data Scientist to optimize cross-system fulfilment strategies and lead model development using advanced machine learning techniques. Candidates should have a Master’s degree in a relevant field and experience in Reinforcement Learning, and should be proficient in Python and ML frameworks like PyTorch and TensorFlow. This role performs critical tasks in improving operational efficiencies and will be onsite at Grab's One North office.

Qualifications

• At least 2 years of relevant experience in data science.
• Professional experience or academic publications in ML-related fields.
• Fluent in Python and ML frameworks.

Responsibilities

• Optimize cross-system fulfilment strategies.
• Build advanced DL and LLM models for marketplace conditions.
• Develop ML models for user and driver behaviour prediction.
• Integrate models into real-time production systems.
• Create technical documentation and present findings.

Skills

Reinforcement Learning

Stochastic Control

Behavioural Modelling

Optimisation Under Uncertainty

Python

PyTorch

TensorFlow

Distributed Computing

Education

Master degree in Computer Science or related field

Tools

PyTorch

TensorFlow

Spark

Ray

Job description

Get to Know the Team

The Fulfilment Tech Family is a foundational part of Grab, enabling seamless coordination between our 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 looking for a Senior Data Scientist to join our Fulfilment team and take the lead in Fulfilment strategy optimization – bringing existing optimisation and automation of our pricing, dispatch and supply management policies to the next level.

Get to Know the Role

You’ll optimize cross-system fulfilment strategies, and lead model development with user and driver behavioural prediction, optimisation and reinforcement learning (RL) techniques. Your primary objective will be to enhance marketplace operations by constructing interpretable, adaptable multi-agent RL systems or decision agents that can manage diverse objectives and disruptions.

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’ll design and implement cross-system strategies to improve operational efficiency in fulfilling user demands and boosting driving utilisation.
• You’ll build advanced DL and LLM models to capture the spatial-temporal patterns of dynamic marketplace conditions.
• You’ll develop advanced ML/DL models to predict user and driver behaviours, and use the behavioural insights to inform decision-making and platform interventions.
• You’ll build and deploy interpretable, adaptable optimisation models, RL systems or decision agents, that can handle multi-objectives and real-world disruptions.
• You’ll collaborate with machine learning engineers and backend engineers to integrate the ML/DL, optimisation or RL models into real-time production systems.
• You’ll create technical documents outlining the methodologies and findings of your work. You’ll also present solutions to non-technical stakeholders.

What Essential Skills You Will Need

• You hold a Master degree in Computer Science, Operations Research, Applied Mathematics, or related field with at least 2 years of relevant experience.
• You have professional experience or academic publications in Reinforcement Learning, Stochastic Control, Behavioural Modelling, Optimisation Under Uncertainty.
• You have experience developing and deploying ML models that incorporate online learning, Markov Decision Processes, or simulation-based optimisation.
• You are fluent in Python and ML frameworks (e.g., PyTorch, TensorFlow).
• You are familiar with distributed computing systems or scalable training platforms (e.g., Spark, Ray).
• You can explore new ideas and learn new skills to accomplish tasks.

You may also apply directly to this opportunity via https://smrtr.io/wK9Hd