Manager, Data Science

Location: Singapore (ASP Operating Unit)
Function: Data & Intelligence, OU Digital
Reports To: Senior Director, Data Science

Position Overview

The Manager, Data Science will be responsible for developing and deploying advanced analytics, machine learning, and Agentic AI solutions that drive business performance across the ASEAN & South Pacific (ASP) Operating Unit.

This role combines strong technical expertise in predictive and prescriptive analytics with the ability to partner closely with business stakeholders to solve commercial, marketing, financial, and strategic challenges. The successful candidate will lead the end-to-end delivery of AI-powered analytical products, from problem framing and model development to deployment, adoption, and value realization.

The role will play a key part in advancing the ASP OU's vision of becoming a data-driven and AI-enabled organization by leveraging both traditional AI/ML techniques and emerging Agentic AI capabilities.

Key Responsibilities

Predictive & Prescriptive Analytics

• Develop predictive models that support revenue growth, market share acceleration, demand forecasting, customer growth, and commercial effectiveness.
• Design prescriptive analytics solutions that recommend optimal business actions and resource allocations.
• Apply advanced statistical, machine learning, and optimization techniques to address complex business challenges.
• Translate analytical findings into practical business recommendations for OU and market leadership teams.

AI & Machine Learning Solutions Delivery

• Build, validate, and deploy machine learning models across commercial, marketing, finance, strategy, and franchise domains.
• Develop scalable AI solutions leveraging structured and unstructured enterprise data.
• Continuously improve model accuracy, performance, explainability, and business relevance.
• Support productionization of AI solutions through collaboration with external partners.

Agentic AI Enablement

• Design and implement Agentic AI solutions that automate analysis, insight generation, and business decision support.
• Develop AI agents capable of reasoning across multiple data sources and analytics products.
• Implement Retrieval-Augmented Generation (RAG), orchestration frameworks, and decision-support agents.
• Partner with business teams to identify high-value use cases where autonomous or semi-autonomous AI agents can improve productivity and decision quality.
• Contribute to the evolution of AI-enabled business processes and self-service analytics capabilities.

Analytics Product Development

• Translate business requirements into scalable analytics products.
• Support development of reusable models, analytics frameworks, and AI accelerators.
• Partner with Product Managers, Data Engineers, and Business SMEs in agile delivery teams.
• Ensure solutions deliver measurable business outcomes and drive user adoption.

Stakeholder Partnership

• Collaborate with cross-functional stakeholders across Strategy, Franchise, Finance, Marketing, Commercial, and Digital teams.
• Support business case development and value quantification for analytics initiatives.
• Communicate complex analytical concepts in clear business language.
• Train and enable business users on AI-driven decision-making tools.

Required Qualifications

Education

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research, or related field.
• Master's degree preferred.

Experience

• 5-8 years of experience in Data Science, Machine Learning, Advanced Analytics, or related fields.
• Demonstrated experience delivering AI and analytics solutions from ideation through production deployment.
• Experience working with business stakeholders to solve commercial or operational problems using data.

Technical Skills

Traditional AI & Machine Learning

• Supervised and Unsupervised Learning
• Time Series Forecasting
• Classification & Regression Models – knowledge of Bayesian is preferred
• Ensemble Methods (Random Forest, XGBoost, LightGBM)
• Deep Learning
• Optimization (Pyomo, PuLP, scipy)
• Model Explainability (SHAP, LIME)

Agentic AI & Generative AI

• Large Language Models (LLMs)
• Agentic AI Frameworks
• RAG Architectures
• Prompt Engineering
• AI Orchestration
• Multi-Agent Systems
• Copilot and Enterprise AI Platforms

Technology Stack

• Python
• SQL
• Azure AI Services
• Databricks
• Microsoft Fabric
• Power BI
• Git
• MLFlow
• MLOps Tooling