Head of Quantitative Research

As Range's Head of Quantitative Research, you will own the research and modeling behind how every member's portfolio gets built, rebalanced, and managed for taxes, from the models under our portfolio construction to the rules that make tax-loss harvesting and direct indexing work at scale. You will report to our CIO, working closely with Investment Operations and Engineering to move your research into production and with Compliance to keep it within our fiduciary obligations.

We're excited to hire this role in Range's New York City office. All of our positions follow an in-office schedule Monday through Friday, allowing you to collaborate directly with your team. If you're not currently based in the area but love what you see, let's discuss relocation as part of your journey to joining us.

Own the design and ongoing refinement of Range's core portfolio construction methodology, including asset allocation, security selection, and rebalancing logic

Build rigorous backtests to validate model changes, weighing tradeoffs between tracking error, tax efficiency, and transaction costs

Lead research on tax-aware strategies, including tax-loss harvesting and direct indexing, and turn the findings into production rules

Partner with Engineering to turn research into production code, and monitor live portfolio and rebalancing behavior after launch

Evaluate new data sources, market data vendors, and modeling approaches that could improve member outcomes

Present research findings and model changes to the CIO and Investment Committee in plain terms

Share your insights beyond our walls by contributing to published market commentary, and representing Range at conferences and in the media

Work with Compliance and Legal to confirm new strategies hold up under our Advisers Act obligations and Range's fiduciary standard

9+ years of experience in quantitative research, portfolio management, or investment strategy, ideally at a digital wealth management or asset management firm

Bachelor's degree in a highly quantitative field such as statistics, mathematics, economics, or financial engineering; advanced degree or CFA progress a plus

Strong grounding in portfolio theory, including mean-variance optimization, factor models, and risk-adjusted return analysis

Fluency in Python and SQL for research and production work; familiarity with portfolio risk tools like Barra, Axioma, or Aladdin, or data vendors like Bloomberg or FactSet, a plus

Direct experience building or maintaining tax-aware investment strategies, including tax-loss harvesting or direct indexing

Experience with tax-aware long/short extensions and concentrated stock diversification

A track record of shipping research into production, not just publishing findings; FinTech, RIA, or broker-dealer experience a plus

Clear communication skills, with comfort moving between deep research and fast turnaround requests