Head of Data Science

Risewave Consulting, Inc.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Risewave Consulting, Inc. is seeking a Head of Data Science to lead advanced data science and ML initiatives for pricing, allocation, and fulfillment across the platform.

The role blends technical depth with strategic business thinking, collaborating with Product, Engineering, Operations, and Commercial teams to architect scalable optimization systems and define long-term marketplace intelligence vision.

Qualifications

• Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, Operations Research, Economics, or a related quantitative discipline.
• 8–12+ years of experience in Data Science, Machine Learning, Applied Research, or Quantitative Modeling.
• Proven track record of delivering production-grade ML solutions that generate measurable business outcomes.
• Strong programming proficiency in Python and SQL.
• Deep expertise in statistical inference, predictive modeling, and experimentation.
• Experience working with large-scale datasets in cloud environments.

Skills

Pricing models

Econometric modeling

Causal inference

Reinforcement learning

Optimization techniques

Predictive modeling

Experimentation

Education

Master’s/PhD in quantitative field

Tools

Python

SQL

AWS

GCP

Job description

As the Head of Data Science, you will spearhead the development of advanced data science and machine learning solutions that optimize pricing, allocation, and fulfillment strategies across the platform. You will address some of the most complex business challenges, driving measurable improvements in customer experience, operational efficiency, profitability, and marketplace health.

This role blends deep technical expertise with strategic business thinking. You will collaborate closely with Product, Engineering, Operations, and Commercial teams to architect scalable optimization systems, build predictive models, shape product strategy, and define the long-term vision for marketplace intelligence capabilities.

Key Responsibilities

• Develop dynamic pricing models that balance customer value, demand elasticity, profitability, and competitive positioning.
• Design pricing algorithms leveraging econometric modeling, causal inference, reinforcement learning, or optimization techniques.
• Evaluate pricing experiments and quantify business impact through rigorous statistical analysis.
• Build pricing simulators to forecast revenue, margin, and customer behavior under varying pricing strategies.
• Partner with Product and Business teams to define pricing guardrails and governance frameworks.
• Design intelligent allocation algorithms to optimize supply-demand matching.
• Develop optimization models for inventory allocation, merchant assignment, workforce distribution, or resource planning.
• Enhance allocation efficiency using mathematical optimization, operations research, and machine learning.
• Analyze marketplace dynamics and pinpoint bottlenecks affecting fulfillment quality.
• Build predictive models for delivery time estimation, order completion, cancellation risk, and fulfillment capacity.
• Identify opportunities to lower fulfillment costs while upholding customer satisfaction.
• Design decision-support systems that streamline operational workflows.
• Lead the development of production-grade ML models from ideation through to deployment.
• Architect large-scale experimentation frameworks and causal analysis methodologies.
• Define the long-term data science roadmap for Pricing and Fulfillment.
• Serve as the technical authority on complex modeling approaches.
• Mentor senior data scientists and champion best practices across the organization.
• Foster an experimentation culture and drive evidence-based decision-making.

Job Requirements

• Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, Operations Research, Economics, or a related quantitative discipline.
• 8–12+ years of experience in Data Science, Machine Learning, Applied Research, or Quantitative Modeling.
• Proven track record of delivering production-grade ML solutions that generate measurable business outcomes.
• Strong programming proficiency in Python and SQL.
• Deep expertise in statistical inference, predictive modeling, and experimentation.
• Experience working with large-scale datasets in cloud environments.