Quantitative Research Associate

Teacher Retirement System of Texas (TRS)•Austin, TX

1d•Onsite

About The Position

The Quantitative Research team within the Quantitative Equity Group (QEG) conducts deep research with the goal of enhancing and innovating across the internal systematic investment platform within the Investment Management Division of TRS. The team is seeking a Quantitative Research Associate with strong research, programming, and finance fundamentals to contribute across the investment lifecycle—from research ideation and implementation to portfolio construction and investment-system infrastructure. This role requires strong statistical foundations and advanced Python programming skills, with a demonstrated ability to deliver research in a production environment. Demonstrated interest or experience in quantitative and/or fundamental investment strategies is preferred. The successful candidate will partner with senior researchers, portfolio managers, and software engineers to enhance production investment systems, and to advance the firm’s alpha, risk, and implementation frameworks. This role offers an opportunity to have a direct impact on approximately $13 billion invested in QEG’s quantitative equity beta-one active extension strategies, and more broadly on approximately $50 billion in investment exposure managed by QEG.

Requirements

Bachelor’s degree from an accredited college or university in a quantitative field such as mathematics, statistics, computer science, engineering, physics, economics, finance, or an equivalent quantitative discipline, demonstrating the ability to conduct original doctoral-level quantitative research, including advanced statistical modeling, machine learning, optimization, and empirical research design.
Three (3) years of full-time directly related, progressively responsible experience in a professional capacity conducting applied quant finance research or related experience.
Three (3) years of full-time directly related, progressively responsible experience with Python or related experience.
Statistics/econometrics, computer science/machine learning, and advanced mathematical foundations (linear algebra, calculus, probability).
Quantitative software development, including proficiency in Python (or a comparable language) and experience with version control and collaborative development (e.g., Git/GitHub).
Investment concepts, terminology, styles, models, strategies, and fundamental factors.
Searching, evaluating, and synthesizing large datasets; perform complex statistical analyses; and preparing concise reports and written and oral recommendations.
Planning, organizing, and prioritizing work to manage a high-volume workload in a fast-paced, changing environment while delivering accurate, detail-oriented results.
Communicate complex information clearly and accurately, both verbally and in writing, using sound judgment and appropriate discretion.
Leveraging LLM tools in day-to-day work to improve productivity.
Collaborate effectively in an applied quantitative investment research environment.
Execute quantitative investment research projects from problem definition through implementation, in partnership with senior investment professionals.

Nice To Haves

A Master’s degree from an accredited college or university in mathematics, statistics, computer science, engineering, physics, economics, finance, or a related field may substitute for two (2) years of the required experience.
A Doctorate (PhD) from an accredited college or university in mathematics, statistics, computer science, engineering, physics, economics, finance, or a related field may substitute for five (5) years of the required experience.
A Doctorate (PhD) from an accredited college or university in a quantitative field such as mathematics, statistics, computer science, engineering, physics, economics, finance, or a related field, or expected completion of a PhD within the current calendar year.
Experience with, or strong interest in, financial markets, quantitative investing, and/or fundamental investing.

Responsibilities

Researches and develops enhancements to systematic investment processes across the pipeline, including alpha generation, signal construction/combination, risk modeling, portfolio optimization, transaction cost modeling, and implementation.
Builds Python-based tools and workflows to access, clean, and analyze data at scale.
Integrates machine learning (ML) algorithms and large language models (LLMs) into the quantitative investment process.
Presents research methods, results, and recommendations to senior researchers and leadership; addresses questions and defends conclusions with data analysis and mathematical formulation.
Partners with portfolio managers and software engineers to deliver end-to-end research and production improvements.
Contributes to the team’s Python-based research and production codebases.
Develops proficiency in the team’s Python-based codebases, including its architecture, data flows, and core investment logic.
Maintains, debugs, and enhances the codebases to support evolving investment and operational requirements.
Partners with software engineers to ensure production systems are robust, scalable, and transparent, with appropriate monitoring and controls.
Incorporates market and liquidity insights into portfolio implementation decisions and risk-aware adjustments.