Senior Data Scientist

Get to know our Team: GrabFin is an aggregate of FinTech businesses spread across 6 countries in South East Asia, in the Payments, Lending and Insurance domains.

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

We are excited to provide financial services to all participants of the Grab 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.

GrabFin stations its data science team across Bangalore and Singapore.

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

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

We will ask you to expect to solve hard technical problems and grow into an expert on batch, real-time, and LLM-driven Data Science use cases.

You 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 Payments architecture and its nuances across use cases and countries.

This understanding will help build predictive models for payment method recommendations, downtime management, intelligent transaction retry, payments processor selection, and other such solutions.

These solutions aim to increase payments transactions and reduce failure rates.

Leverage advanced LLMs to power the next generation of payment experiences.

Manage the entire end-to-end lifecycle of predictive models and AI agents, from development to deployment and optimization.

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/Master of Laws research and new frameworks and incorporate them into practical applications.

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 online learning.

Engineer features from internal data assets to build refined transaction and customer profiles.

Generative AI Development: Design and deploy Generative AI solutions using prompt engineering, agent orchestration, and model fine-tuning (e.g., LoRA, PEFT).

Build evaluation datasets and define guardrails for ongoing evaluation and monitoring.

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.

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, or LangChain) and LLM orchestration.

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 Follow 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.