NISA manages $295 billion in fixed income and equity securities plus $167 billion in derivatives notional value for tax-exempt and taxable institutional clients. The tech stack reveals a quantitative operation: Bloomberg, AWS, Databricks, PySpark, Python, C#, MATLAB, FactSet, and Aladdin underpin portfolio modeling and risk analytics. Active hiring across engineering, data, and risk—alongside projects in backtesting infrastructure, feature engineering frameworks, and model deployment—signals scaling of in-house quantitative capability rather than outsourced dependency.
NISA is an employee-owned investment advisor founded in 1994, headquartered in St. Louis, Missouri, with over 400 employees and more than 20% holding participation interests. The firm specializes in customized investment solutions, particularly liability-driven investing strategies and risk-controlled portfolios, serving some of the world's largest institutional investors. NISA manages both traditional securities (fixed income, equities) and complex derivatives overlays. The business operates across portfolio management, risk mitigation, compliance, and trade lifecycle operations, with explicit pain points in portfolio risk management, model risk, regulatory adaptation, and data integration.
As of March 31, 2026, NISA manages $295 billion in fixed income and equity securities and $167 billion in derivatives notional value for institutional clients.
NISA's core tools include Bloomberg, AWS, Databricks, PySpark, Python, C#, SQL Server, PostgreSQL, MATLAB, FactSet, and Aladdin. The firm is adopting Concur, ADP Workforce Now, and NetSuite.
NISA is based in St. Louis, Missouri, and is 100% employee-owned with over 400 employees.
NISA Investment Advisors, LLC's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.