Quantitative Analyst

About the job
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 ETFs and mutual funds with portfolio holdings, institutional ownership, advisory firms, financial advisors, and asset managers. These relationships are analyzed across more than 10,000 funds, 23,000+ advisory firms, and 400,000+ financial advisors to support fund discovery, portfolio analytics, and asset-manager distribution intelligence.

As a Quantitative Analyst, you will work across quantitative investment research, financial data analysis, portfolio construction, and applied data science. The role covers fund selection, fund substitution, performance analysis, portfolio optimization, ownership analysis, and the development of research tools used across PortfolioFuture.

You will work with large financial datasets and apply machine learning, quantum optimization, and large language models (LLMs) to improve research, analysis, reporting, and decision support for investors, financial advisors, and asset managers.

What you’ll work on

• Develop quantitative models for ETF and mutual fund research, fund selection, and fund ranking.
• Analyze fund performance, risk, fees, market exposures, portfolio holdings, and ownership data.
• Research fund substitution and identify investments that can serve similar roles within a portfolio.
• Study performance persistence and test whether historical investment relationships remain useful out of sample.
• Analyze fund returns using portfolio replication, investable factor exposures, tracking error, and residual performance.
• Work with SEC 13F filings, Form ADV/IAPD data, advisory-firm data, institutional holdings, and other large financial datasets.
• Develop portfolio construction and large-scale portfolio optimization methods.
• Apply machine learning to investment research, prediction, classification, ranking, anomaly detection, and data quality.
• Use large language models and artificial intelligence for financial research, information extraction, analytical reasoning, and reporting.
• Explore quantum optimization and other computational methods for portfolio allocation and financial optimization.
• Build and improve Python-based research and data pipelines.
• Test models across different market periods, investment strategies, and datasets to evaluate robustness and out-of-sample performance.

What we’re looking for

• Background in quantitative finance, financial engineering, statistics, econometrics, mathematics, computer science, data science, economics, or a related field.
• Strong Python skills and experience analyzing large datasets.
• Knowledge of statistical modeling, financial econometrics, time-series analysis, portfolio optimization, and empirical research methods.
• Ability to turn investment and portfolio questions into quantitative analyses.
• Experience or interest in investment research, portfolio management, asset management, wealth management, ETFs, mutual funds, or systematic investing.
• Familiarity with machine learning, artificial intelligence, large language models, or optimization methods.
• Ability to evaluate model results, data quality, statistical significance, and out-of-sample performance.
• Ability to communicate quantitative research and investment findings clearly.
• Experience with financial market data, fund data, portfolio holdings, SEC filings, 13F data, Form ADV/IAPD, or other asset-management datasets is useful but not required.
• Experience or research interest in quantum computing, quantum algorithms, or quantum optimization is a plus.

PortfolioFuture research
Fund Substitution researches which ETFs and mutual funds can credibly serve as alternatives for the same role in a portfolio.

Fund Decomposition analyzes how much of a fund’s historical return can be reproduced with investable market exposures and what remains after those exposures are accounted for.

PortfolioFuture’s research is independent. Commercial relationships do not affect which funds are surfaced, how they rank, or the conclusions reached.

Apply through https://www.linkedin.com/jobs/view/4476245376/