Multi-Asset Strategies Researcher (Senior Analyst or Associate)

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

2d•Onsite

About The Position

The Multi-Asset Strategies Researcher conducts research and analysis across multi-asset investment strategies, develops quantitative models, evaluates portfolio performance and risk, and generates investment insights to support asset allocation decisions. The role analyzes market trends, economic conditions, and regulatory developments; prepares research reports and presentations; supports investment decision-making; and helps improve research methodologies and analytical frameworks. The researcher works with the Multi-Asset Strategies team, Investment Operations, Investment Management Division staff, and TRS partners. The role conducts quantitative research to develop alpha signals, portfolio construction, and risk management across Alternative Risk Premia and Risk Parity portfolios. It designs and executes research from hypothesis through robustness testing and sizing recommendations; evaluates external and academic research; documents and presents findings; and integrates large language models into quantitative investment research. The researcher builds Python-based data tools and workflows, works with portfolio managers and quantitative developers on research and production improvements, contributes to shared data, backtesting, and portfolio construction systems, and applies engineering standards such as version control, testing, code review, and documentation. The role also supports model runs and rebalances, monitors risk, exposures, and performance, investigates data and model exceptions, and provides backup coverage for critical production processes. Related work may be assigned.

Requirements

Bachelor’s degree from an accredited college or university in a related field; a high school diploma or equivalent plus additional full-time quantitative research or related experience may substitute on an equivalent year-for-year basis.
One to two years of full-time experience in quantitative research or related experience; a master’s or doctoral degree in a closely related field may substitute on an equivalent year-for-year basis.
Knowledge of probability, statistics, econometrics, linear algebra, and calculus.
Knowledge of research design and empirical finance research pitfalls, including overfitting and multiple testing.
Knowledge of portfolio construction, risk modeling, and performance attribution concepts.
Knowledge of investment concepts, terminology, strategies, and fundamental factors.
Knowledge of software engineering principles applied to quantitative research code.
Advanced use of Python and data science libraries including pandas, NumPy, SciPy, and scikit-learn.
Ability to write SQL queries and design schemas, and to manipulate and validate large datasets.
Ability to maintain software in a version control system such as Git, including testing and code review.
Ability to collaborate in an applied quantitative investment research environment, exercise independent judgment, take ownership of an assigned strategy or asset-class area, solve technical problems independently, and maintain effective working relationships.
No registration, certification, or licensure is required.

Nice To Haves

Master’s degree or PhD in mathematics, statistics, computer science, physics, engineering, financial engineering, economics, or a related area.
Intermediate experience with Python.
Experience with SQL and version control software, including Git.
Experience with data science-oriented Python libraries such as pandas, SciPy, NumPy, and scikit-learn.
Experience taking research code into a production investment process.
Knowledge of or strong interest in futures, FX, and derivatives markets.
Demonstrated original research output, such as a thesis, publication, or a signal taken into production.

Responsibilities

Conduct quantitative research to develop and enhance alpha signals, portfolio construction, and risk management across Alternative Risk Premia and Risk Parity portfolios.
Design and execute the research lifecycle from hypothesis through robustness testing and sizing recommendation.
Evaluate external and academic research for internal application and generate novel ideas to broaden strategies and signals.
Produce research documentation explaining theory and empirical results.
Present and defend findings to portfolio management, the MAS Investment Committee, and senior management.
Integrate large language models into the quantitative investment research process.
Build Python-based tools and workflows to access, clean, and analyze data at scale.
Partner with portfolio managers and quantitative developers to deliver research and production improvements.
Contribute to the shared multi-asset data system, backtesting framework, and portfolio construction tools.
Apply engineering standards, including version control, unit testing, code review, and documentation.
Support daily and periodic model runs, rebalances, and data quality checks.
Build and maintain risk, exposure, and performance monitoring for assigned strategies.
Investigate and resolve data and model exceptions.
Provide backup coverage for critical production processes.
Perform related work as assigned.