Senior Data Scientist

Job Summary

The Role:

• Develop an understanding of Grab, Financial Services and Banking data and its nuances across countries. This understanding will help build predictive models for Financial Services product cross-sell, hyper-personalised messaging and channel optimization.
• Use LLMs to power the next generation of customer experiences.
• Manage the entire end-to-end lifecycle of ML models and AI agents, from development, deployment, optimization and maintenance.
• Work with engineering, compliance & operation teams to create solutions and product changes, informed by your findings and business inputs.
• Stay current with the latest AI/LLM research and new frameworks and incorporate them into practical applications.
• Work as an individual contributor or in a team to solve complex problem statements.
• Individual contributor role with 5+ years of experience expected.

Responsibilities

• Predictive Modelling: Build predictive models using machine learning to solve for classification, ranking and sequential learning. Engineer features from internal data assets to build refined transaction and customer profiles.
• MLOps & Automation: Build end-to-end MLOps pipelines to automate model training, deployment, and monitoring. Track production performance and implement feedback loops for continuous improvement.
• Generative AI Development: Design and deploy Agentic solutions using prompt engineering, agent orchestration, and LLM fine-tuning (e.g., LoRA, PEFT). Build evaluation datasets and define guardrails for ongoing evaluation and monitoring.
• Ownership and Collaboration: Design AI solutions by working backwards from our needs. Lead the delivery of AI capabilities, from concept through production deployment

Qualifications

The Must Haves:

• Core ML: Expert proficiency in Python, Spark, and SQL (Presto/Hive) with a command of fundamental ML concepts (Bagging, Boosting, Online Learning, Recommendation Engines).
• Generative AI & Agent Frameworks: Hands-on experience with Agentic frameworks (specifically LangGraph, LangSmith, LangChain), LLM orchestration and evals
• MLOps & Productionization: Expertise in productionizing ML solutions with MLOps tools (MLflow, Kubeflow, TFX, SageMaker) and deploying scalable components on cloud platforms.
• Real-Time Streaming: Experience in the real-time implementation of machine learning solutions using Flink SQL and Flink SDK is a good to have

Adherence to clean coding, modular design, and version control (Git/GitHub) characterises Engineering Excellence. Additionally, it involves the ability to balance model performance with business trade-offs, such as latency, cost, and scalability.

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