Lead Data Scientist

2 weeks ago

Singapore Data Analytics Full time $180,000 - $250,000 per year

Get to Know the Team

At Grab, the Market Insights team drives impactful data-driven decisions by uncovering insights about the region's users, merchants, and driver partners. By analysing vast datasets across transactional and behavioural activities, we bridge the gap between raw data science capabilities and actionable business strategies. Our work is pivotal in driving growth and enhancing the on-demand industry, always innovating through AI/ML integration for practical business applications.

Get to Know the Role

As a Lead Data Scientist (Analytics), your role involves implementing advanced machine learning systems to improve user engagement, merchant operations, and driver strategies. You'll be a technical leader and problem solver, able to deploy data models into production and guiding team members in the best practices for AI and machine learning applications. Your expertise will directly influence product decisions and shape Grab's competitive edge in the on-demand economy. You'll be part of an enthusiastic and collaborative team, reporting to the Head of Analytics - User & Market Insights at our One North Singapore office.

The Critical Tasks You will Perform

You will:

  • Collaborate with engineers, product managers, marketing teams, and regional country teams to convert business challenges into machine learning solutions.
  • Lead AI initiatives to enhance internal productivity, streamline workflows, and automate tasks within the User & Market Insights team.
  • Analyze and interpret complex datasets of transactional, behavioural, and user interactions to uncover trends and actionable insights that inform growth strategies.
  • Develop and productionise scalable machine learning models, ensuring solutions can handle large datasets while being adaptable to changing business requirements.
  • Fine-tune and implement LLM-based use cases, customising pre-trained large language models for Grab-specific applications.
  • Optimise ML frameworks and statistical methods, such as TensorFlow, PyTorch, and causal inference for robust modelling needs.
  • Manage MLOps Lifecycles, automating processes from data preparation, evaluation, training, and serving models in production environments.
  • Mentor team members, providing technical guidance and sharing knowledge to elevate the team's capabilities.

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