AI Investment Researcher

Company Description
PortfolioFuture is an independent fund discovery and intelligence company focused on empirical research into ETFs and mutual funds. The organization evaluates and ranks funds to identify where they can credibly compete for allocations in investor and advisor portfolios. By analyzing fund substitution, relative performance, and investable return decomposition, PortfolioFuture helps market participants understand what individual funds truly add to a portfolio. For asset managers, the company connects independent fund research with portfolio and ownership intelligence to highlight competitive product positioning and distribution opportunities. All research is strictly independent, with rankings and findings unaffected by commercial relationships, reflecting a core belief that better products deserve a chance to win.
Role Description
The AI Investment Researcher internship is a remote role focused on applying data-driven and computational techniques to fund and portfolio analysis. Interns will support the design and implementation of AI-based methods to detect patterns in fund performance, substitution opportunities, and portfolio construction outcomes. Day-to-day responsibilities include gathering and cleaning financial and market data, conducting statistical and empirical research, and building prototypes or tools that help evaluate ETFs and mutual funds. The role involves collaborating with the research team to test hypotheses, interpret results, and document insights in clear, investor-friendly formats. Interns may also contribute to internal reports, visualizations, and exploratory analyses that enhance PortfolioFuture’s fund intelligence platform.
Qualifications
Strong foundation in Computer Science and Data Science, including familiarity with algorithms, data structures, and data analysis workflows.
Skills in Pattern Recognition and Statistics to identify, model, and evaluate performance and allocation patterns in financial datasets.
Demonstrated Research abilities, particularly in empirical or quantitative domains, with an emphasis on careful methodology and reproducibility.
Proficiency in at least one programming language commonly used in data science (such as Python or R) and experience with relevant libraries (e.g., NumPy, pandas, scikit-learn).
Interest in financial markets, ETFs, mutual funds, or portfolio analysis; coursework or self-study in finance or economics is beneficial.
Ability to work independently in a remote environment, manage multiple tasks, and communicate findings clearly in written and verbal form.
Current enrollment in or recent completion of a degree program in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or related discipline.

Work arrangement
Yes