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Uvimco

Investment Analyst/Associate - Asset Allocation and Risk Management

Charlottesville, Virginia, United States

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hirly's read of this role

Role family
Finance
Seniority
Mid level
Country
US
Work mode
On-site / unstated
First seen by hirly
22 Oct 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

University of Virginia Investment Management Company (UVIMCO) provides investment management services to UVA and its associated organizations. We are responsible for evaluating, selecting, and monitoring investment strategies for a $18 billion endowment portfolio. Our portfolio is invested globally across a broad range of established and emerging asset classes and strategies including public equity, hedge funds, private equity, venture capital, real estate, resources, credit, fixed income, and derivatives. The vast majority of our investments are implemented through long-term partnerships with world-class external fund managers. Additional information about UVIMCO can be found at www.uvimco.org .

Position Overview

The Analyst/Associate role is an opportunity to work directly with senior management in a small, flat, investment organization. The primary role is to be the internal expert on the risk and asset allocation database and analytical systems, as well as to drive further development of these systems, increasingly through the application of AI. They will also lead consulting projects involving quantitative research, custom programming, or AI tooling.

The Asset Allocation and Risk Management (AARM) team has four primary responsibilities:

Risk measurement: estimating risk metrics and exposures for the total portfolio and sub-strategies. AARM utilizes a proprietary risk system, and this responsibility includes utilizing, maintaining, and further developing this internal risk system, including the integration of machine learning and other AI techniques where they improve on traditional approaches.

Risk management: synthesizing outputs of the risk measurement processes for UVIMCO’s Chief Investment Officer and Risk Committee. This includes recommending portfolio management actions and assisting in the management of UVIMCO’s overlay portfolio.

Consulting: providing quantitative research and analysis to other internal UVIMCO groups as well as to outside stakeholders.

AI enablement: utilize AI capabilities to strengthen the investment process, including developing applications that structure information from manager documents and filings, and utilizing them for portfolio management purposes.

Primary Responsibilities

Work collaboratively with the team to help generate attractive long-term real returns for the Long Term Pool while remaining within the risk tolerance of the University. Participate as a member of the Risk and Investment Committees;

Design, build, and validate AI applications that enhance the investment process, including LLM-based pipelines that extract structured data from unstructured sources, and AI-assisted tooling that automates recurring research and reporting workflows;

Lead the development of UVIMCO’s data management and risk management systems by improving or developing new methodologies for risk measurement and asset allocation models, incorporating statistical learning and machine learning approaches where they add measurable value;

Lead the data management efforts for risk and asset allocation data, including maintaining and improving the security master database;

Work with UVIMCO’s IT team to implement new models, AI services, and database components into production using best practices;

Work with internal asset class teams to tailor risk models for each asset class;

Assist in the management of UVIMCO’s derivatives overlay; and

Provide quantitative support to the CIO, Risk Committee, and other Staff for research projects.

Position Requirements

The candidate must be a motivated self-starter and possess strong organizational skills, a strong work ethic, attention to detail, and a high level of professionalism and integrity. The candidate will have a strong technical background, and experience using R, SQL, Python, and AI models to complete full-stack model development projects. Demonstrated success in independent programming projects is required, and experience applying AI tooling to real analytical problems is strongly preferred.

Additionally, the candidate should have:

An undergraduate degree from a top institution in a quantitative discipline such as Statistics, Economics, Physics, Engineering, Finance, Computer Science, Data Science, Applied Mathematics, Machine Learning, or Operations Research. A post-graduate degree in one of these fields is desired but not required.

A CFA Charter or demonstrated interest in pursuing the CFA Program.

Excellent written and verbal communication skills.

Demonstrated the ability to work effectively on an independent basis as well as in a collaborative team environment.

Two to four years of experience in quantitative finance, preferably involved with the portfolio management of endowments or other institutional investors. Candidates with zero to two years of experience in quantitative finance will be considered for an analyst-level role.

Familiarity with AI and model risk considerations, including explainability, reproducibility, benchmarking and evaluation questions related to deploying AI in an investment setting.

Experience with building HTML/web reports (e.g. using Shiny or Dash) or the desire to learn.

Please note that this position is based in-office in Charlottesville, VA.

The starting base salary range is $120,000 - $135,000 for the Analyst position and $160,000 - $175,000 for the Associate position. This salary range listed reflects the expected base salary for this position at the time of posting. Individual compensation will be based on factors such as relevant experience, education, skills, qualifications, and other job-related considerations.

Original posting on Uvimco's site ↗

Listed on hirly, a job board. hirly is not the employer: Uvimco is hiring for this role.

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