Quantitative Analyst

About the role

PortfolioFuture builds investment research products around a difficult problem: helping investors determine whether an ETF or mutual fund deserves attention, understand what drives its returns, and identify better alternatives.

The Quantitative Analyst helps improve how PortfolioFuture identifies and evaluates investment opportunities.

You will work with fund returns, market data, and PortfolioFuture’s existing research methods to test which relationships are meaningful, which signals remain useful out of sample, and how our investment discovery process can become more accurate over time.

The work focuses on developing and validating quantitative methods that help PortfolioFuture identify which funds deserve further attention.

What you’ll work on

• Analyze ETFs and mutual funds using historical returns, benchmarks, exposures, fees, risk characteristics, and other investment data.

• Develop and test methods for identifying funds that are genuinely comparable rather than relying only on categories or labels.

• Evaluate whether differences in performance between comparable funds persist beyond the period in which they were identified.

• Research fund replication, tracking error, residual performance, and other measures that help distinguish common market exposure from differentiated results.

• Test which historical relationships and signals provide useful information about future fund behavior.

• Evaluate PortfolioFuture’s research methods across different periods, market environments, fund types, and assumptions.

• Investigate unexpected results, unstable relationships, and cases where quantitative evidence changes the initial investment view.

• Develop new quantitative approaches to improve fund discovery, comparison, and evaluation.

• Help translate research findings into methods that can be applied consistently across a large fund universe.

What we’re looking for

• Strong foundation in statistics, econometrics, quantitative finance, mathematics, computer science, data science, economics, or a related discipline.

• Ability to turn an investment question into a testable quantitative problem.

• Strong understanding of statistical inference, estimation uncertainty, robustness, and out-of-sample validation.

• Proficiency with Python and experience working independently with data.

• Strong analytical judgment and the ability to evaluate results critically.

• Interest in ETFs, mutual funds, portfolio construction, asset management, or empirical investment research.

• Strong attention to detail.

• Ability to communicate quantitative findings, assumptions, and limitations clearly.

Prior experience in quantitative finance is valuable but not required. We care more about rigorous thinking, empirical judgment, and the ability to determine what the evidence supports.

About PortfolioFuture

PortfolioFuture develops independent investment research and analytical tools for evaluating funds and portfolio alternatives. ETF Alternatives (https://portfoliofuture.com/etf-alternatives) helps investors identify comparable funds worth further research, while ETF Replication (https://portfoliofuture.com/etf-replication) separates fund returns into investable exposures and residual performance.

Our quantitative research helps PortfolioFuture discover and evaluate investments that may otherwise be overlooked.