Quantitative Associate

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DUMAC is the investment office that manages a multi‑billion dollar global portfolio on behalf of Duke University and other pools of capital. Our mission is to provide critical financial support to the university for professorships, student scholarships, and pioneering research. The Data Science & Risk team serves as a central quantitative hub, partnering with all investment groups to drive data‑informed decision‑making, enhance portfolio construction, and manage risk.

DUMAC is seeking a highly motivated and intellectually curious Quantitative Associate/Analyst to join our Data Science & Risk team. This role is designed to provide deep, cross‑functional exposure to all aspects of institutional investing. You will work on high‑impact projects that span our Public, Private, and Direct investment portfolios, as well as our back‑office operations.

This position offers a robust training ground for a career in quantitative finance and asset management, with the potential for long‑term advancement within the organization for exceptional performers. You will be a key contributor to the team, bringing fresh perspectives and technical skills to solve complex investment challenges.

Education:

Work requires communications and analytical skills generally acquired through completion of a bachelor's degree program. An MBA and CFA are preferred.

Work requires experience in research and financial analysis, generally acquired through two years of related experience. OR AN EQUIVALENT COMBINATION OF RELEVANT EDUCATION AND/OR EXPERIENCE.

• Analyze and model portfolio exposures, performance, and risk across a diverse range of asset classes.
• Develop quantitative models to assess investment manager factor exposures and identify drivers of return.
• Contribute to private asset cash flow forecasting and pacing models to inform portfolio construction and liquidity management.
• Research, develop, and backtest systematic investment strategies in collaboration with investment teams.
• Explore and implement AI/ML techniques to enhance investment decision‑making and optimize operational workflows.
• Design, build, and maintain interactive dashboards and visualizations to communicate complex data and insights to stakeholders across the organization.
• Collaborate with investment professionals and operational teams (Finance, Legal) on a variety of cross‑functional projects.

• Bachelor’s or Master’s degree in a quantitative field such as Finance, Economics, Statistics, Computer Science, Engineering, or Mathematics.
• A demonstrated passion for investing and a strong desire to learn about financial markets.
• Strong proficiency in R or Python for data analysis, modeling, and automation.
• Excellent communication skills with the ability to articulate complex technical concepts to non‑technical audiences.
• An independent thinker with a proactive mindset and a high degree of intellectual curiosity.
• Experience building dashboards and data visualizations using tools like R Shiny, Python Dash, Tableau, or Power BI.
• Familiarity with financial markets, different asset classes, and investment concepts is a plus, but not required.
• Prior internship or project experience in finance, asset management, or a related quantitative field for Quantitative Analyst; 3–5‑year relevant work experience for Quantitative Associate.
• Exposure to machine learning libraries (e.g., scikit‑learn, Tensor Flow, PyTorch) and techniques.
• Experience with database querying (e.g., SQL).

• Impact:
  Play a critical role in supporting the mission of a world‑class university.
• Exposure:
  Gain unparalleled experience across public and private markets, working directly with seasoned investment professionals.
• Growth:
  Join a collaborative, mentorship‑driven environment with a clear path for professional development and learning.
• Culture:
  Be part of a dynamic, intellectually stimulating team that sits at the intersection of technology and finance.

Duke University provides an annual base salary range for this position as USD $0.00 to USD $0.00. Duke University considers factors such…