Quantitative Analyst

PortfolioFuture is an independent fund discovery and intelligence company. We use empirical research to discover, evaluate, and rank ETFs and mutual funds, and identify where they can credibly compete for allocations in portfolios.
Our research connects more than 10,000 funds with institutional investors, 20,000+ advisory firms, and 400,000+ financial advisors, supporting fund discovery, portfolio analytics, and asset-manager distribution intelligence.
As a Quantitative Analyst, you will work with over 130 million fund relationships and 290+ million ownership opportunities, applying quantitative finance, machine learning, large language models (LLMs), and large-scale optimization methods to investment research and decision-making.
What you’ll work on
Develop quantitative models for ETF and mutual fund research, selection, ranking, and substitution.
Analyze fund performance, risk, fees, and holdings using statistical modeling, portfolio replication, and residual performance analysis.
Research performance persistence, return similarity, and out-of-sample investment outcomes.
Analyze SEC 13F filings, Form ADV/IAPD data, and institutional holdings to identify investment and distribution opportunities.
Develop portfolio construction and large-scale portfolio optimization methods.
Apply machine learning, LLMs, and AI to financial research, prediction, ranking, and reporting.
Explore quantum optimization and build Python-based research and data pipelines.
What we’re looking for
Background in quantitative finance, financial engineering, statistics, mathematics, computer science, economics, or a related field.
Strong Python skills and experience with large datasets, statistical modeling, and time-series analysis.
Knowledge of investment research, portfolio theory, financial econometrics, and optimization.
Familiarity with machine learning, AI, LLMs, or advanced computational methods.
Ability to evaluate model robustness, statistical significance, and out-of-sample performance, and communicate findings clearly.
Experience with ETFs, mutual funds, SEC filings, institutional holdings, or quantum optimization is a plus.
About PortfolioFuture
Fund Substitution identifies ETFs and mutual funds that can serve similar portfolio roles, using return similarity, relative performance, tracking error, and fees.
Fund Decomposition separates returns explained by investable market exposures from residual performance, evaluating replication quality, residual alpha, and risk.
Fund Opportunity ranks ETFs and mutual funds across four dimensions: Similar Fund Ranking, Addressable Market, Pure Alpha, and Diversification Benefits.
PortfolioFuture's research is independent. Commercial relationships do not affect which funds are surfaced, how they rank, or the conclusions reached.

Work arrangement
Yes