Multi-Asset Strategies Researcher (Senior Analyst or Associate)

Teacher Retirement System of Texas•Austin, TX

1d•Onsite

About The Position

The Multi-Asset Strategies Researcher at the Senior Analyst or Associate level 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 enhance research methodologies and analytical frameworks. It works proactively with the Multi-Asset Strategies team, Investment Operations, Investment Management Division staff, and TRS partners. The researcher conducts quantitative research to develop and enhance alpha signals, portfolio construction, and risk management across Alternative Risk Premia and Risk Parity portfolios. The role supports the full research lifecycle, from hypothesis development through robustness testing and sizing recommendations; evaluates external and academic research for internal application; and produces research documentation and presentations for portfolio management, the MAS Investment Committee, and senior management. It also integrates large language models into quantitative investment research. The position builds Python-based tools and workflows for accessing, cleaning, and analyzing data at scale; collaborates with portfolio managers and quantitative developers on research and production improvements; and contributes to shared multi-asset data, backtesting, and portfolio-construction systems. Portfolio-management support includes model runs, rebalances, data-quality checks, risk and performance monitoring, investigating exceptions, and providing backup coverage for critical production processes. Other related work may be assigned.

Requirements

Bachelor’s degree from an accredited college or university in a related field, or a high school diploma or equivalent with additional full-time quantitative research or related experience substituted 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 be substituted on an equivalent year-for-year basis.
Knowledge of probability, statistics, econometrics, and foundational mathematics, including linear algebra and calculus.
Knowledge of research design and practical pitfalls of empirical finance research, including overfitting and multiple testing.
Knowledge of portfolio construction, risk modelling, and performance attribution concepts.
Knowledge of investment concepts, terminology, strategies, and fundamental factors.
Knowledge of software engineering principles as applied to quantitative research code.
Ability to work collaboratively in an applied quantitative investment research environment.
Ability to exercise independent judgment on research design and interpretation of empirical results.
Ability to take ownership of an assigned strategy or asset-class area and develop subject-matter expertise.
Ability to solve technical problems independently and proactively.
Ability to establish and maintain effective working relationships with colleagues, agency staff, and external contacts.

Nice To Haves

Master's degree or doctorate/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.
Advanced use of Python and the data science stack, including pandas, NumPy, SciPy, and scikit-learn.
SQL query writing and schema design, and experience manipulating and validating large datasets.
Experience maintaining software in a version control system such as Git, including testing and code review.

Responsibilities

Conduct quantitative research to develop and enhance alpha signals, portfolio construction, and risk management for Alternative Risk Premia and Risk Parity portfolios.
Design and execute the research lifecycle, from hypothesis through robustness testing and sizing recommendations.
Evaluate external and academic research for internal application and generate ideas that broaden the strategy and signal set.
Produce high-quality 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 shared multi-asset data systems, backtesting frameworks, 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.