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This team provides quantitative modeling solutions for private markets across a wide variety of problems, including nowcasting private asset and fund values, forecasting alpha in private asset deals, and optimizing strategic asset allocation with illiquid assets. The team builds and connects innovative models and methodologies to support investment decision-making in illiquid asset classes.
Job Responsibility:
Lead the research, design, and development of new Nowcasting and valuation models across additional private asset classes
Conduct empirical research to calibrate models to private market data and evaluate performance using backtesting, benchmarking, and robustness analysis
Build, maintain, and productionize model codebases, ensuring reliability, transparency, and scalability
Partner with internal stakeholders to define use cases, communicate model behavior and limitations, and support integration into investment and analytics workflows
Contribute to research publications, conference presentations, and client-facing discussions highlighting the team’s modeling innovations
Support broader Private Markets SWAT Team initiatives, including bespoke modeling projects for portfolio management or advisory teams
Requirements:
Ph.D. in Finance, Economics, Statistics, or a related quantitative field
3+ years of experience in empirical financial modeling or quantitative research in industry or academia
Strong foundation in empirical modeling, including time-series analysis and Bayesian methods
Proficiency in Python and willingness to adopt new tools, libraries, and technologies
Demonstrated ability to conduct rigorous empirical research, manage complex datasets, and design well-structured analyses
Effective communicator capable of explaining quantitative concepts to diverse audiences and collaborating across technical and nontechnical teams
Nice to have:
Experience with private markets or empirical asset pricing