Quantitative Engineer (Private MarketsThis Jobot Job is hosted by: Jim Quirk
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Salary: $300,000 - $360,000 per year
A bit about us:We are an elite, well-funded fintech startup building the foundational data, pricing, and transaction infrastructure for the global private markets. Backed by a $16M Series A from the world's largest asset managers and investment banks, our platform tracks over $300B in proprietary transaction data and $4T in funding signals for 100,000+ companies. Based in San Francisco, our lean team thrives on technical rigor, deep engineering ownership, and solving complex, multi-trillion-dollar valuation challenges for the world's most sophisticated institutional investors.
Why join us?- Competitive Base Salary!
- Extremely Competitive Benefits!
- Flexible Work Schedules!
- Accelerated Career Growth!
Job DetailsLocation: San Francisco, CA (In-Office 4 Days/Week)Compensation: $300,000 - $350,000 + Equity About the Company We are a rapidly growing, recently funded Series A fintech company building the data, pricing, and transaction infrastructure for the global private markets. Unlike public equities, private company shares do not trade continuously on centralized exchanges, making accurate pricing and market intelligence extremely difficult to establish. We are solving this challenge by aggregating vast amounts of proprietary secondary-market activity, funding data, and investor signals into a platform used by the world's leading financial institutions. Backed by a $16M Series A round from major global asset managers and investment banks, our platform currently tracks: Over $300 billion in proprietary transaction data Approximately $4 trillion in historical funding-round data Comprehensive coverage of more than 100,000 private companies and investors An elite institutional client base that collectively manages over $52 trillion in assets The Opportunity This is not a traditional internal quantitative role. It is a rare opportunity to apply institutional-quality modeling to proprietary, highly unique private-market data. You will blend rigorous quantitative research with production data engineering and direct customer exposure, enjoying massive ownership within a lean, high-caliber team. As a Quantitative Engineer, you will take full ownership of the models and data infrastructure powering our flagship private-market intelligence products. A primary focus will be our predictive daily pricing engine for actively traded private companies. Because these are illiquid assets without clean, public price discovery, this work requires exceptional creativity and the ability to extract highly accurate signals from complex, imperfect datasets. Responsibilities Model Development: Build, maintain, and continuously improve complex pricing and valuation models for illiquid private-market assets. Feature Engineering: Identify, test, and integrate new financial features and data signals to drive model accuracy. Production Pipelines: Develop and optimize scalable production data pipelines that support machine learning models and client-facing products.AI Innovation: Experiment with Large Language Models (LLMs) to automate and scale data ingestion, entity classification, and automated quality assurance. Client Advisory: Analyze massive proprietary datasets to deliver clear insights and join select technical conversations to explain methodology directly to sophisticated financial clients. Product Ownership: Collaborate closely with a tight-knit engineering team to materially shape the technical architecture and product roadmap. Key Requirements Financial Rigor: Previous quantitative experience at a trading desk, hedge fund, investment bank, or a similarly rigorous, data-driven financial environment. Mathematical Foundation: Strong background in statistics, applied mathematics, financial modeling, machine learning, or quantitative research. Technical Stack & Data Engineering: Robust programming skills with hands-on experience building and maintaining production-grade data pipelines. Communication Skills: Ability to articulate complex technical methodologies and modeling choices clearly to highly sophisticated financial clients. Education: Degree in Data Science, Mathematics, Statistics, Computer Science, Financial Engineering, or a closely related field. A Master's degree or PhD is strongly preferred. Location: Must be based in or willing to relocate to San Francisco, working on-site in the office four days per week. Why This Role is Compelling Instead of maintaining a fractional component of a mature, legacy public-market trading system, you will have end-to-end ownership over the models, signals, and pipelines defining a completely new asset class infrastructure. Backed by top-tier institutional investors, this role provides the technical depth of a top hedge fund with the high-upside equity and velocity of an elite San Francisco startup.
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