全职 Senior Data Scientist (Supply Planning) 工作, 薪水, Grab 公司招聘中 - Ricebowl

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 and effectively. As the data science team, we'll leverage AI technologies to provide driver partners with strategic, data-driven instructions and incentives, so as to optimize the supply utilization given the various market conditions.

Get to Know the Role

As Senior Data Scientist, you'll look into the driver-facing problems and transform these user / business problems into DS problems. You'll use big data to derive actionable insights and design solutions. You'll develop deep learning, reinforcement learning, and optimisation models to solve challenges related to supply repositioning, activation, and incentivisation within Grab's marketplace.

This position will be reporting to the Senior Data Science Manager and be based onsite in Grab One North Singapore office.

The Critical Tasks You Will Perform

• You'll collaborate with stakeholders to identify the business problems and requirements about driver repositioning, activation and incentivisation.
• You'll translate the business problems into data science problems, formulate them and design potential solutions.
• You'll retrieve and analyse relevant data for exploratory analysis, feasibility studies, and solution prototyping.
• You'll develop and deploy scalable data science solutions, and measure their impact via experiments.
• You'll build dashboards to keep monitoring the health of DS systems, and continuously maintain and improve the solutions.

What Essential Skills You Will Need

• You have at least 3 years of relevant experience and a Degree in computer science, computer engineering, statistics, data science, applied mathematics, economics, Operations Research (OR), or other related computational fields.
• You are familiar with developing Extract, Transform, and Load (ETL) data pipelines to support complex mathematical modelling and optimisation tasks.
• You are familiar with the mathematical optimisation lifecycle, possessing hands-on experience translating business problems into rigorous OR models using Linear Programming (LP), Mixed-Integer Linear Programming (MILP), or Non-Linear Programming (NLP).
• You have experience designing and implementing custom heuristics or meta-heuristics (e.g., Genetic Algorithms) for computationally expensive, large-scale optimisation problems where exact solutions are not feasible.
• You have good understanding of the software development lifecycle and engineering practices, with experience writing readable, maintainable, and testable code (including hands-on experience with unit and model testing).