Description
Grab is Southeast Asia's leading superapp, offering various services such as food delivery, financial management, and transportation.
We are looking for a Senior Data Scientist to help construct in-house machine learning platforms and deploy production-grade ML and LLM solutions to solve complex business problems at scale.
Responsibilities
- Architect Scalable ML Solutions: Design, develop, and benchmark efficient deep learning and machine learning algorithms for large-scale prediction, classification, and forecasting problems.
- Deploy Production LLMs and Pipelines: Build, fine-tune, and deploy state-of-the-art LLMs and ML pipelines into production platforms in collaboration with engineering teams.
- Process & Feature Engineering: Analyse massive, multi-modal datasets and build data processing pipelines using distributed computing frameworks.
- Measure Business Impact: Design and execute offline evaluations and online A/B experiments to validate model accuracy, latency, and business performance metrics.
- Drive Platform Innovation: Stay at the forefront of machine learning and GenAI research, contributing to technical IP, prototypes, and team best practices.
- Cross-Functional Integration: Partner with Product, Engineering, and Operations stakeholders to translate business requirements into scalable ML features.
- Leverage AI for Productivity: Utilize GenAI tools and modern development workflows to accelerate code delivery, pipeline testing, and technical documentation.
Requirements
- Education & Domain Expertise: A Master's or PhD in Computer Science, Operations Research, Mathematics, Data Science, or a related quantitative field.
- Relevant Track Record: At least 2 years of hands-on industry experience building, deploying, and monitoring machine learning or deep learning models in production environments.
- Technical & Coding Proficiency: Programming skills in Python and PyTorch, alongside practical experience with SQL and Spark for distributed data processing.
- End-to-End Pipeline Knowledge: Understanding of data extraction, transformation, and pipeline engineering (ETL) within large-scale data platform architectures.
- LLM & Deep Learning Experience: Hands-on experience developing or fine-tuning transformer models, NLP architectures, or modern LLM frameworks.
- Stakeholder & Analytical Translation: Demonstrated ability to translate ambiguous business and transaction challenges into concrete, measurable ML formulations.
- Mandatory AI Productivity: Experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas.
Benefits
- Term Life Insurance and comprehensive Medical Insurance
- GrabFlex benefits package
- Parental and Birthday leave
- Love-all-Serve-all (LASA) volunteering leave
- Confidential Grabber Assistance Programme
- FlexWork arrangements