Senior Data Scientist

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.

Life at Grab

We care about your well-being at Grab, here are some of the global benefits we offer:

• We have your back with Term Life Insurance and comprehensive Medical Insurance.
• With GrabFlex, create a benefits package that suits your needs and aspirations.
• Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
• We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.

What we stand for at Grab

We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.