Responsibilities
The Opportunity
A senior data science leadership opportunity is available within a technology-driven organisation where advanced analytics and machine learning are central to complex, real-time commercial and operational decision-making.
The role will provide technical and strategic leadership across a portfolio of quantitative problems involving pricing, demand and capacity balancing, resource allocation, and service fulfilment. The successful candidate will be expected to translate sophisticated statistical and machine learning techniques into production systems with measurable commercial and operational outcomes.
This position combines deep quantitative expertise, algorithmic problem-solving, technical leadership, and strong business judgement. It will involve close collaboration with senior stakeholders across product development, technology, operations, and commercial functions.
Core Areas of Responsibility
Pricing & Quantitative Decision Models
- Lead the development and evolution of analytical models that support dynamic pricing and other real-time commercial decisions.
- Apply statistical inference, causal methods, experimentation, optimisation, and advanced machine learning to understand behavioural and market responses.
- Develop approaches for balancing commercial performance, demand responsiveness, and overall ecosystem health.
- Establish appropriate methodologies for evaluating the causal impact of pricing and other interventions.
Optimisation & Resource Allocation
- Provide technical direction for algorithms that continuously balance demand with available capacity.
- Develop optimisation and decision models covering allocation, assignment, incentives, and utilisation of constrained resources.
- Apply operations research and machine learning techniques to complex, high-volume decision environments.
- Improve the efficiency and robustness of automated decision-making under changing supply, demand, and capacity conditions.
Service & Fulfilment Analytics
- Lead predictive modelling initiatives designed to improve the accuracy and reliability of service-time estimates.
- Develop analytical solutions addressing preparation or processing times, delays, cancellations, and other operational risks.
- Identify opportunities to improve service reliability while reducing avoidable operational costs.
- Translate model outputs into practical interventions through close partnership with operational and technology teams.
Data Science Leadership
- Define the longer-term technical direction for data science across pricing, optimisation, and operational decision systems.
- Act as a senior technical authority on machine learning, statistical modelling, experimentation, and quantitative decision science.
- Set standards for model development, validation, experimentation, and deployment.
- Promote rigorous use of causal analysis and empirical evidence in business decision-making.
- Provide technical mentorship and guidance to experienced data scientists and researchers.
- Help identify high-value analytical opportunities and shape the broader data science roadmap.
Requirements
Technical & Professional Profile
Essential
- Advanced academic qualification in a quantitative discipline such as statistics, computer science, economics, operations research, data science, or a closely related field.
- Extensive professional experience in data science, machine learning, applied research, quantitative modelling, or algorithm development within technically sophisticated environments.
- Demonstrated experience taking machine learning or statistical models from research through to reliable production deployment.
- Strong programming and data analysis capability, including Python and SQL, together with experience working with scalable computing or cloud-based technology environments.
- Deep understanding of statistical inference, experimental methodology, and quantitative model evaluation.
- Experience leading technically complex data science initiatives with meaningful commercial or operational impact.
- Ability to operate effectively across technical and non-technical stakeholder groups and translate complex analytical concepts into practical business decisions.
Preferred
- Experience working on problems involving dynamic pricing, marketplace optimisation, matching, allocation, logistics, capacity planning, or other highly dynamic operational systems.
- Exposure to two-sided or multi-participant platforms where supply, demand, capacity, and customer behaviour interact continuously.
- Experience applying causal inference, reinforcement learning, econometric modelling, optimisation, or operations research to real-world decision systems.
- Experience operating machine learning models in environments requiring high availability, substantial transaction volumes, and rapid decision-making.
- Experience mentoring senior technical professionals and influencing data science strategy beyond an individual project or team.
What Success Looks Like
The successful leader will:
- Establish a coherent technical direction for quantitative decision science across pricing and operational optimisation.
- Improve the quality and rigour of experimentation, causal analysis, and model evaluation.
- Advance the use of machine learning and optimisation in complex, real-time decision processes.
- Translate research and analytical innovation into dependable production capabilities.
- Strengthen collaboration between data science, engineering, product, operations, and commercial stakeholders.
- Develop senior technical talent and raise the overall standard of quantitative problem-solving.
Candidate Profile
This opportunity is suited to an established data science or quantitative modelling leader who combines research depth with strong production experience. Candidates should be comfortable addressing ambiguous, high-dimensional problems, challenging existing assumptions through empirical analysis, and taking ownership from mathematical formulation through deployment and measurable business outcomes.
Relevant backgrounds may include advanced data science, machine learning, algorithmic decision-making, quantitative research, econometrics, operations research, optimisation, or applied computational research in large-scale technology environments.
Application: Apply to this job posting, and send your CV with the job title as the subject line to: [email protected] & https://www.linkedin.com/in/treasa-wong/