Chief Data Scientist

ELLIOTT MOSS CONSULTING PTE. LTD.; Singapore; On-site; SGD 150,000 - 250,000; Full time

Job summary

A leading consultancy firm in Singapore is seeking a Chief Data Scientist to enhance its Data & Analytics strategy. This senior role entails defining the organization’s data roadmap, overseeing architecture, and leading large-scale AI/ML initiatives. The ideal candidate should have over 15 years of relevant experience, focusing on strategic stakeholder engagement and managing complex data programs. Competitive compensation and opportunities for professional growth are offered.

Qualifications

• Minimum 15–18 years in Data & Analytics, with 8–10 years in leadership roles.
• Proven experience leading digital transformation programmes.
• Experience in regulated industries is an advantage.

Responsibilities

• Define the organization's Data & Analytics roadmap.
• Architect data platforms and oversee their design and implementation.
• Lead delivery of complex data & analytics programmes.

Skills

Data Science & AI/ML

Cloud Platforms (AWS/Azure/GCP)

Stakeholder Management

Leadership

Communication Skills

Tools

Data Warehouses

Data Lakes

Enterprise BI tools

Job description

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 Account Management to support go‑to‑market efforts, RFP responses, and solution proposals.
• Build and maintain strategic partnerships with technology OEMs, hyperscalers, and fintech / analytics partners.

6. Team Leadership & Capability Building

• Build and lead a high‑performing team of data scientists, data engineers, solution architects, and project/programme managers.
• Define competency frameworks, career paths, and mentoring structures for Data & Analytics talent.
• Foster a culture of innovation, continuous improvement, and delivery excellence through Agile/Lean practices (Scrum, Kanban, DevOps/DataOps).

7. Governance, Risk & Compliance

• Chair or participate in risk and delivery governance forums to ensure compliance with contractual, regulatory, and internal standards.
• Ensure all data initiatives meet relevant security, privacy, and compliance requirements (including those in highly regulated sectors such as financial services, government, and telco where applicable).
• Implement and monitor KPIs and OKRs for data initiatives, with clear linkage to business outcomes.

Required Qualifications & Experience

• Professional ExperienceMinimum 15–18 years of relevant experience in Data & Analytics, with at least 8–10 years in senior leadership roles.
  Proven experience leading large-scale digital transformation programmes involving enterprise Data Warehouses, Data Lakes, big data platforms, cloud data platforms, and AI/ML initiatives.
  Demonstrated track record managing regional or multi-country teams and delivery operations, including P&L ownership and portfolios in the range of tens of millions of USD.
  Experience in turning around under-performing or distressed projects and delivering to contractual and business objectives.
  Strong exposure to regulated industries such as financial services, government, telecommunications, logistics, or public sector is an advantage.

Technical Skills

• Deep expertise in: Data Science & AI/ML: supervised/unsupervised learning, model development, evaluation, deployment, and monitoring.
  Data Platforms: Data Warehouses, Data Lakes, Data Lakehouse architectures, ETL/ELT, real-time streaming, big data ecosystems.
  Cloud Platforms: Experience with at least one major cloud (AWS/Azure/GCP) for data and analytics workloads.
  Analytics & BI: Enterprise BI tools, dashboards, reporting, self‑service analytics.
  Data Engineering: data integration, ingestion pipelines, orchestration, and performance optimisation.
• Strong understanding of DevOps/DataOps, CI/CD, test automation, and Agile delivery models.

Leadership & Soft Skills

• Strategic thinker with the ability to align technical roadmaps with business strategy and P&L targets.
• Excellent stakeholder management and communication skills, including engagement at CXO and Board level.
• Strong people leadership: team building, mentoring, succession planning, and cross-cultural team management.
• Proven negotiation, conflict resolution, and risk management skills in complex, multi-stakeholder environments.
• High level of integrity, professionalism, and accountability.