Chief Data Scientist

1 week ago

Singapore ELLIOTT MOSS CONSULTING PTE. LTD. Full time

Job Description The Chief Data Scientist will lead the organization's Data & Analytics strategy, architecture, and execution, with a mandate to build enterprise-grade data platforms and deliver advanced analytics, AI/ML and business intelligence solutions that drive measurable business outcomes. This is a senior leadership role with end-to-end accountability for data science initiatives, including solution design, delivery governance, stakeholder management, and team leadership across multiple geographies. The role holder is expected to operate at CXO level, influence strategic decisions, and manage large, complex transformation programmes. Key Responsibilities 1. Data & Analytics Strategy and Leadership Define and own the organisation’s Data & Analytics roadmap, including data science, AI/ML, big data, and business intelligence initiatives. Translate business strategy into data-driven programmes, aligned with revenue, profitability, and operational efficiency goals. Establish and lead an enterprise data science function, including operating model, standards, and best practices. 2. Enterprise Data Platforms & Architecture Architect and oversee the design, implementation, and modernisation of large-scale data platforms (Data Lake, Data Warehouse, Lakehouse, real-time data pipelines, cloud data platforms). Govern data quality, data governance, metadata management, and security practices in collaboration with Technology, Security, and Compliance teams. Drive cloud migration and platform modernisation journeys (e.g., on-prem to cloud / hybrid, big data platforms, MLOps / DataOps practices). 3. Advanced Analytics, AI & Machine Learning Lead the design and delivery of advanced analytics and AI/ML solutions (e.g., predictive models, fraud risk analytics, customer analytics, forecasting, optimisation). Evaluate and select appropriate algorithms, tools, and technologies for specific use cases, ensuring robustness, scalability, and explainability. Establish MLOps frameworks for model lifecycle management (development, deployment, monitoring, re‑training). 4. Programme Delivery & P&L Ownership Own delivery of large and complex data & analytics programmes, including timelines, budgets, scope, and quality. Manage P&L for the Data & Analytics portfolio, including cost optimisation, utilisation, and profitability targets. Lead turnaround of distressed/at-risk projects, implementing corrective action plans and stakeholder recovery strategies. 5. Stakeholder & Client Engagement Engage and influence C-suite and senior business stakeholders to identify, prioritise, and shape high-impact data initiatives. Collaborate with Sales, Product, and

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