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Global brand and metro location increase competition, but senior niche optimisation focus reduces candidate density.
Marketplace yield/pricing and decision-science specialization limits transferability across industries.
Lead-level domain experience, mandatory optimisation use cases, and production cloud analytics imply strict hiring filters.
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Lead design, optimisation, and evolution of yield, pricing, and matching models to improve forecasting accuracy, stability, and explainability.
Analyse large-scale historical trading and behavioural datasets to identify patterns and opportunities for performance improvement.
Guide and mentor senior data scientists while partnering with commercial, engineering, and product leaders to embed data-driven decisioning in operational workflows.
Proven experience leading yield, pricing, or optimisation use cases in large-scale marketplaces or data-rich environments.
Advanced expertise in statistical modelling and proficiency in Python or R.
Experience with cloud-based data platforms and scalable analytics environments (e.g., AWS, Azure, Redshift).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in forecasting, optimisation, and decision science within discrete choice or trading contexts.
Capable of balancing deep technical leadership with people management and cross-team collaboration.
Comfortable translating complex statistical concepts into clear, practical business insights for non-technical stakeholders.