Get to Know the Team

Grab's supply planning team is dedicated to enhancing the experience of our driver partners, ensuring they can drive smartly. As the data science team, we use AI technologies to provide driver partners with strategic, data-driven instructions and incentives, so as to improve the supply utilisation given the market conditions.

Get to Know the Role

As Lead Data Scientist, you will lead DS development work and coordinate the collaboration with different functional teams (Engineers, Product Manager, Designer). You will use big data to derive actionable insights and design solutions. You will develop deep learning, reinforcement learning, and optimisation models to solve challenges related to supply repositioning, activation, and incentivisation within Grab's marketplace. You will report into the Senior Data Science Manager and work onsite in Grab One North office.

The Critical Tasks You Will Perform

• You will collaborate with stakeholders to identify business problems and requirements related to driver repositioning, activation, and incentivisation.
• You will translate business problems into data science problems, formulating and designing potential solutions.
• You will create technical documents outlining methodologies and findings, and present solutions to business stakeholders to ensure agreement.
• You will design overall architectures for solutions in collaboration with different technical teams, develop data science solutions, and lead integration with Engineering systems.
• You will participate in code and design reviews to maintain high development standards.
• You will design and conduct experiments to evaluate the impact of solutions.
• You will mentor junior team members.

What Essential Skills You Will Need

• You have a Master's Degree in computer science, computer engineering, artificial intelligence, statistics or related fields.
• You have at least 5 years of experience in AI / ML engineering, developing AI-powered products or platforms.
• You have a foundation in modern AI methodologies like deep learning, reinforcement learning, and generative models.
• You have mastery in MLOps and AI infrastructure, with experience in scalable system architectures, model training pipelines, and cloud platforms (AWS, GCP, Azure).
• You are proficient in Python and knowledgeable in large-scale data processing (Spark, Ray, Dask), along with software engineering practices like CI/CD.