Senior Data Scientist

Company Description

About Grab and Our Workplace

Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.

Job Description

Get to Know the Team

The Business & Transaction Platform Team is dedicated to empowering our regional merchants by leveraging cutting-edge machine learning (ML) and large language models (LLM). Our focus is on creating and implementing innovative solutions that provide insights, recommendations, and useful tools to drive growth and enhance the daily operations of our merchants. We are committed to exploring new opportunities and potential use cases for LLMs to help merchants boost their sales and streamline their business processes.

Get to Know the Role

We are looking for a Senior Data Scientist to join our team and help build ML and LLM-based applications that assist merchants in managing their daily business activities. You'll provide recommendations, insights, and domain knowledge to resolve business challenges and explore various potential use cases for LLMs to enhance merchant sales. You'll work with the latest technologies and make a significant impact on our merchants' success.

The Critical Tasks You Will Perform

You will:

• Design, build, and deploy large language model (LLM) applications that are scalable and reusable.
• Design and execute data-driven experiments to validate the effectiveness of LLM-based solutions.
• Leverage cutting-edge Machine Learning (ML) algorithms to analyze massive datasets and resolve challenging business issues.
• Collaborate with cross-functional teams to identify and explore new opportunities for LLM applications that can drive merchant growth.
• Monitor and integrate the latest advancements in LLMs and other ML technologies to improve our existing solutions.

Qualifications

What Essential Skills You Will Need

You have:

• A Master's degree in Computer Science, Operations Research, Mathematics or a related field.
• At least 3 years of relevant work experience in Data Science, with expertise in areas such as recommendation systems, classification, or predictive modelling, is required.
• Proficiency in programming languages such as Python, PyTorch, SQL, and Spark.
• Experience with data Extracting, Transforming, and Loading (ETL) processes and experience with large data platforms.
• Experience implementing and optimising LLM-specific techniques, such as Prompt Engineering, Retrieval-Augmented Generation (RAG), and fine-tuning.
• Deep, practical experience designing and deploying models in at least two key domains: Recommendation Systems and Time-Series/Predictive Modeling.
• The ability to decompose ambiguous business challenges into clear, quantifiable data science hypotheses and execution plans.
• Skills in translating complex model results, technical limitations, and business recommendations into clear, concise narratives for executive and non-technical audiences.

Additional Information

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.