Senior Data Scientist

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, deploying, and managing critical data science models on a daily basis. We will ask you 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.

Responsibilities:

• 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 optimisation.
• 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, optimisation, and maintenance.
• Work with engineering, compliance and operations 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 classification, ranking and sequential learning. Engineer features from internal data assets to build refined transaction and customer profiles.
• MLOps and 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 and Agent Frameworks: Hands-on experience with Agentic frameworks (specifically LangGraph, LangSmith, LangChain), LLM orchestration and evals.
• MLOps and 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.
• Following 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.

Skills

LangChain, LangGraph, LangSmith, Python, Spark