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The Head of Data Science will lead the development of a globally scalable, AI-enabled data science capability within Investment Technology Solutions (ITS), delivering advanced analytics and machine learning solutions that directly enhance investment outcomes across asset classes.
Reporting to the Head of ITS, this role will bridge front-office investment teams and enterprise technology-ensuring that data science capabilities are platform-based, industrialized, and embedded into portfolio construction, risk management, and investment operations workflows.
The mandate combines three core objectives:
Deliver measurable impact to investment performance and risk management. Build and scale an enterprise-grade data science and AI platform. Establish a globally collaborative operating model aligned to ITS strategy.
Key Responsibilities
Investment-Focused Delivery
- Partner closely with CIOs, Portfolio Managers, and Research Heads to translate investment challenges into scalable analytical solutions.
- Develop and productionalize alpha signals, risk models, optimization engines, liquidity analytics, and scenario modelling capabilities.
- Ensure analytics are embedded within portfolio construction, trading, and risk systems (e.g., Aladdin, Wall Street Office, Axioma or equivalent platforms).
- Drive quantifiable improvements in performance attribution, risk-adjusted returns, drawdown management, and portfolio efficiency.
Data Science Platform & Architecture
- Design and implement a robust, cloud-enabled data science platform supporting:
- Research and experimentation environments
- Feature stores and reusable signal libraries
- Model development, validation, and testing frameworks
- MLOps and model lifecycle management
- Deployment pipelines into investment and risk platforms
- Ensure architecture supports cross-asset reuse, security, auditability, and regulatory compliance.
- Align platform standards with broader ITS data and infrastructure strategy.
Enterprise & Cross-Functional AI Enablement
- Collaborate with Risk, Finance, Operations, and Distribution teams to extend AI capabilities where aligned to investment technology priorities.
- Contribute to enterprise AI initiatives including stress testing automation, operational intelligence, and advanced reporting analytics.
- Represent ITS Data Science in enterprise AI governance and model risk forums.
- Promote responsible AI principles including explainability, transparency, and bias mitigation.
Organizational Build & Global Scale
- Establish and scale a high-performing global data science organization embedded within ITS.
- Develop a federated delivery model supporting regional investment teams across market hours.
- Create clear differentiation between quantitative research, data science, AI engineering, and ML platform engineering roles.
- Implement strong talent development pathways to build deep capital markets and vendor platform expertise.
Product Mindset & Value Realization
- Operate data science as a product capability, with defined roadmaps, prioritization frameworks, and measurable value tracking.
- Establish adoption metrics and performance KPIs for all deployed solutions.
- Balance near-term market support needs with longer-term platform innovation.
Governance & Controls
- Implement robust model validation, monitoring, and lifecycle management processes.
- Ensure compliance with model risk management standards and regulatory expectations.
- Maintain data lineage transparency and documentation standards aligned with ITS governance frameworks.
Candidate Profile
Experience
- 10+ years' experience in asset management, capital markets, or quantitative investment technology environments.
- Demonstrated leadership building and scaling data science or quantitative analytics teams within a technology-enabled operating model.
- Proven track record delivering production-grade AI/ML solutions embedded in investment platforms.
- Experience operating in regulated financial services environments.
Capital Markets Expertise
- Deep understanding of:
- Multi-asset portfolio construction and optimization
- Risk modelling and stress testing
- Market structure, liquidity dynamics, and execution considerations
- CFA designation strongly preferred.
- Advanced degree (PhD/MSc) in quantitative fin