Role: Head of Data Science (Pricing & Fulfillment Optimization)

Location: Singapore

Type: Full-time

About the Role

We are seeking an visionary Head of Data Science to lead the development of advanced machine learning and mathematical optimization solutions across our platform. In this high-impact executive role, you will tackle our most complex marketplace challenges—optimizing dynamic pricing, supply-demand allocation, and fulfillment efficiency to directly drive profitability, operational excellence, and customer satisfaction.

Bridging deep technical research with executive business strategy, you will partner directly with Product, Engineering, Operations, and Commercial leaders to design scalable intelligent systems and shape the future of our marketplace engine.

Key Responsibilities

Dynamic Pricing Strategy: Architect, deploy, and scale real-time dynamic pricing algorithms using econometric modeling, causal inference, reinforcement learning, and mathematical optimization to balance demand elasticity with unit economics.
Allocation & Marketplace Matching: Build intelligent supply-demand matching systems, inventory allocation engines, and workforce/resource planning models leveraging Operations Research and advanced ML.
Fulfillment Operations: Develop predictive models for delivery time estimation (ETA), cancellation risk, fulfillment capacity, and bottleneck reduction to maximize marketplace efficiency at minimal cost.
Experimentation & Simulation: Design enterprise-grade experimentation frameworks, pricing simulators, and causal analysis methodologies to forecast revenue, margins, and marketplace health under complex scenarios.
Technical Leadership & Governance: Establish data science roadmaps, serve as the technical authority for complex modeling architectures, set governance guardrails, and mentor senior/principal data scientists across the organization.

Key Requirements

Education: Master's or PhD in Data Science, Computer Science, Statistics, Mathematics, Operations Research, Economics, or a related quantitative discipline.
Experience: 8–12+ years of hands-on experience in Data Science, ML, Applied Research, or Quantitative Modeling, with a proven track record of deploying production-grade systems that generate direct business impact.
Technical Stack: Deep expertise in Python, SQL, cloud platforms (AWS/GCP/Azure), predictive modeling, statistical inference, and large-scale optimization.
Domain Expertise: Preferred experience within high-frequency, two-sided marketplaces (e.g., ride-hailing, e-commerce, on-demand delivery, or logistics).

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