Head of Data Science Technology Solutions

At Franklin Templeton, we advance our industry by developing innovative ways to help clients achieve investment goals. Our dynamic firm spans asset management, wealth management, and fintech, offering many opportunities for investors to progress toward their objectives. We welcome diverse perspectives and a flexible culture that empowers employees to reach their potential.

• 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.
• Embed analytics within portfolio construction, trading, and risk systems (e.g., Aladdin, Wall Street Office, Axioma).
• Drive quantifiable improvements in performance attribution, risk‑adjusted returns, drawdown management, and portfolio efficiency.

• 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.

• Collaborate with Risk, Finance, Operations, and Distribution teams to extend AI capabilities in line with 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 such as explainability, transparency, and bias mitigation.

• 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 among quantitative research, data science, AI engineering, and ML platform engineering roles.
• Implement talent development pathways to build deep capital markets and vendor platform expertise.

• 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.

• 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.

• 10+ years 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.

• Deep understanding of:
  • Multi‑asset portfolio construction and optimisation
  • Risk modelling and stress testing
  • Market structure, liquidity dynamics, and execution considerations
• CFA designation strongly preferred.
• Advanced degree (PhD/MSc) in quantitative finance, statistics, mathematics, computer science, or related discipline desirable.

• Strong command of statistical modelling, machine learning, and time‑series analysis.
• Experience integrating alternative datasets into investment workflows.
• Familiarity with cloud‑native data architectures, distributed compute, and MLOps frameworks.
• Experience working with enterprise investment platforms (e.g., Aladdin, Wall Street Office, Axioma) strongly preferred.

• Technol…