Director of Investment Data Science
We are seeking a senior investment-focused Data Science leader to embed advanced quantitative methods, machine learning, and AI across the full investment lifecycle. This role will directly enhance portfolio construction, underwriting, risk management, and capital allocation decisions across public and private markets.
The ideal candidate combines deep technical expertise with strong investment intuition, product thinking, and the ability to partner with senior investment leadership. This is a hands-on, high-impact position charged with elevating the institution into a global benchmark for applied AI within sovereign investing.
Key Responsibilities
Investment & Portfolio-Driven AI Strategy
* Build and own quantitative research pipelines supporting alpha generation, factor research, cross-asset allocation, and tactical/strategic portfolio construction.
* Develop AI-driven models that improve deployment pacing, NAV forecasting, liquidity planning, stress scenarios, and return optimization.
* Modernize investment due diligence by applying AI/ML to GP selection, co-investment underwriting, secondaries pricing, fund benchmarking, and valuation intelligence.
* Deliver investment copilots and real-time analytics systems for deal teams, risk committees, and asset-class heads.
* Partner with CIOs, asset-class leaders, and risk to embed quantitative and AI insights into investment decisions.
Data Science Engineering for Investment Workflows
* Build scalable AI and quant platforms supporting front-office alpha research, private-asset underwriting, and portfolio monitoring.
* Integrate diverse datasets: financial statements, deal flows, GP track records, alternative data, macro series, ESG signals, and unstructured investment materials.
* Establish robust model governance tailored to investment use cases: bias detection in underwriting, scenario stress tests, explainability for IC processes, and model risk controls.
* Ensure systems meet enterprise requirements for auditability, data lineage, reliability, and cybersecurity.
Experience
* 15+ years in applied data science, quantitative research, or AI engineering within investment or trading environments.
* Deep experience applying AI/quant methods to private markets: fund selection, GP analytics, co-investment modeling, secondaries evaluation, and valuation frameworks.
* Proven track record building AI/quant systems that directly influence deal sourcing, underwriting, portfolio construction, monitoring, and exits.
* Experience collaborating with CIOs, PMs, principals, and investment committees in high-stakes settings.