Chief Technology Officer (CTO) - Quant, Data & AI

Position and overview

Compensation

• Compensation is described as a competitive salary plus performance-based incentives and benefits; no salary cadence or amount is specified.

Main responsibilities

• Define and execute long-term technology, data, and AI strategy aligned with commercial growth and investment priorities.
• Establish data architecture, governance, taxonomy, tagging, metadata standards, quality frameworks, and enterprise-wide accessibility.
• Lead quantitative models for investment decisions, portfolio optimization, forecasting, risk management, and business performance analysis.
• Build analytics and AI capabilities spanning machine learning, predictive analytics, LLMs, generative AI, and intelligent automation.
• Develop scalable cloud-native platforms for data processing, quantitative research, model development, and AI-powered services.
• Partner with investment, risk, finance, operations, and commercial teams to produce measurable business outcomes.
• Establish model governance, validation, AI ethics, regulatory compliance, and risk management frameworks.
• Drive digital transformation, process automation, operational efficiency, and data-driven decision-making.
• Evaluate and implement emerging technologies in quantitative analytics and AI.
• Build and lead teams across data engineering, quantitative research, machine learning, software engineering, cybersecurity, and cloud infrastructure.
• Manage budgets, vendors, and external technology, data, research, and AI partnerships.
• Advise executives and stakeholders on technology trends, AI opportunities, data strategy, and competitive developments.

Qualifications

• 15+ years of technology leadership experience with significant exposure to data-centric, quantitative, and AI-driven organizations.
• Experience scaling enterprise data platforms, analytics functions, and AI capabilities in relevant data-intensive sectors.
• Deep expertise in data architecture, governance, metadata management, taxonomy design, and enterprise data tagging.
• Strong background in quantitative modelling, statistics, forecasting, algorithm development, risk analytics, and decision science.
• Experience commercializing AI and machine learning technologies to achieve measurable business outcomes.
• Strong understanding of generative AI, LLMs, MLOps, and responsible AI governance.
• Experience managing large-scale cloud environments and data platforms using AWS, Azure, or Google Cloud.
• Executive-level stakeholder engagement, including boards, investors, regulators, and senior leaders.
• Experience leading global or multi-regional teams and technology transformation programmes in high-growth environments.
• Strong understanding of cybersecurity, data privacy, and relevant regulatory frameworks.
• Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Quantitative Finance, Statistics, or a related discipline.
• An advanced degree (Master’s, MBA, PhD, or equivalent) is highly desirable.

Preferred experience

• Experience in investment management, hedge funds, asset management, private equity, fintech, trading, or quantitative research is preferred, not essential.
• Exposure to alternative data, financial modelling, market analytics, portfolio optimization, and risk management platforms is preferred.
• A track record implementing AI and data strategies across multiple industries is preferred.
• Experience building organizations where data and AI drive revenue and competitive advantage is preferred.