Senior Data Scientist

Get to know the team Grab's Financial Services business is present across 6 countries in South East Asia, in the Payments, Lending, Insurance and Digital Banking domain.

We station our engineering teams in India, Singapore, Indonesia, Malaysia and Vietnam.

We are excited to provide financial services to all participants of the ecosystem be it our Consumers, Drivers or Merchants.

We build our products on fundamental market insights combined with advanced Data Science, Generative AI and engineering to bring the best product market fit across the cross section of our user base.

This understanding of our ecosystem combined with world class engineering execution continues to create tremendous value for our customers.

Financial Services business stations its data science team across Bangalore, Kuala Lumpur and Singapore.

We aim to hire a Data Scientist to join our Bangalore office to expand the existing Bangalore team.

The data scientist will work in a relatively flat team structure with an independent goal of building, deploy and manage critical data science models on a daily basis.

You will be asked to expect to solve hard technical problems and grow into an expert on both batch and real-time Data Science use cases.

You will have experience with technology and data science along with being.

Grab bases this role out of its Bangalore office.

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.

The daily activities: 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

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