Senior Data Scientist — Consumer Personalization & GenAI

Grab; Singapore; On-site; SGD 120,000 - 180,000; Full time

Benefits offered by this job

Health insurance

Gym membership

Job summary

Grab is seeking a data science/ML specialist to enhance user personalization through ranking, translations, and agentic content. You will work with product, marketing, and engineering to deploy ML/LLM solutions, build data pipelines, and ensure observability across production systems.

You will guide end-to-end ML lifecycle activities, including feature selection, tuning, and model validation, leveraging Scikit-Learn, Pandas, XGBoost, TensorFlow, and PyTorch.

Qualifications

• 4+ years in data science or ML with a relevant Master’s degree.
• Proficient in Python, R or Java plus pipeline and ETL experience.
• Apply predictive modeling algorithms understanding production trade-offs for deployment.
• Use ML frameworks such as Scikit-Learn, Pandas, XGBoost, TensorFlow, or PyTorch.
• Manage end-to-end ML lifecycle including feature selection, tuning, and validation.

Responsibilities

• Enhance targeting with relevance ranking, translation models, and agentic content.
• Analyze data to understand user behavior for personalization.
• Collaborate with product, marketing, and engineering on ML and LLM deployment.
• Build models, data pipelines, observability, and evaluation frameworks for production systems.
• Use GenAI and AI coding assistants to accelerate development and evaluation workflows.

Skills

Python

R

Java

ETL

Data pipelines

Scikit-Learn

Pandas

XGBoost

TensorFlow

PyTorch

ML lifecycle

Observability

GenAI

Education

Master's degree

Tools

TensorFlow

PyTorch

XGBoost

Pandas

Job description

Grab is seeking a data science/ML specialist to enhance user personalization through ranking, translations, and agentic content. You will work with product, marketing, and engineering to deploy ML/LLM solutions, build data pipelines, and ensure observability across production systems.

You will guide end-to-end ML lifecycle activities, including feature selection, tuning, and model validation, leveraging Scikit-Learn, Pandas, XGBoost, TensorFlow, and PyTorch.