Company:
Stealth Start Up in the Wealth Tech Space
Compensation:
225-300k + bonus + equity
We are currently partnering with a stealth startup within the WealthTech space who specalizes in building AI Native Portfolio Management tools in search for a Founding Quant to join their organization. We are looking for an individual who has deep experience in portfolio contruction, mangament, and asset allocation in either the retail wealth management space, SMAs, or RIA platforms.
Responsiblities:
Developing portfolio construction and optimization methods that operate effectively under real-world constraints such as taxes, trading frictions, and regulatory requirements
Implementing and refining approaches across risk-based, return-based, and multi-objective optimization frameworks
Building scalable, production-ready Python systems to support rebalancing, risk management, and tax-sensitive decision-making
Designing and validating risk models, covariance estimation techniques, and portfolio constraints used in live portfolios
Encoding U.S. tax considerations — including wash sale rules, capital gains treatment, and asset location — directly into portfolio logic
Using modern AI tools and ML frameworks as part of daily experimentation and research workflows
Translating complex quantitative behavior into clear explanations that can be understood by non-technical stakeholders
Qualifications:
Advanced degree in quantitative field (Masters or Ph.D.)
Experience with Python.
Exposure to advisor-led or retail investment platforms (e.g., SMAs, RIA tools)
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Stealth Start Up in the Wealth Tech Space
Compensation:
225-300k + bonus + equity
We are currently partnering with a stealth startup within the WealthTech space who specalizes in building AI Native Portfolio Management tools in search for a Founding Quant to join their organization. We are looking for an individual who has deep experience in portfolio contruction, mangament, and asset allocation in either the retail wealth management space, SMAs, or RIA platforms.
Responsiblities:
Developing portfolio construction and optimization methods that operate effectively under real-world constraints such as taxes, trading frictions, and regulatory requirements
Implementing and refining approaches across risk-based, return-based, and multi-objective optimization frameworks
Building scalable, production-ready Python systems to support rebalancing, risk management, and tax-sensitive decision-making
Designing and validating risk models, covariance estimation techniques, and portfolio constraints used in live portfolios
Encoding U.S. tax considerations — including wash sale rules, capital gains treatment, and asset location — directly into portfolio logic
Using modern AI tools and ML frameworks as part of daily experimentation and research workflows
Translating complex quantitative behavior into clear explanations that can be understood by non-technical stakeholders
Qualifications:
Advanced degree in quantitative field (Masters or Ph.D.)
Experience with Python.
Exposure to advisor-led or retail investment platforms (e.g., SMAs, RIA tools)
#J-18808-Ljbffr