Quantitative Analyst

Quantitative Analyst

The Quantitative Analyst will design, build, validate, and maintain pricing and risk models for private-market assets, with a primary focus on the ValueAlt pricing engine and the AltLens risk engine. The role combines financial engineering with full-stack software development: you will implement models in production code, calibrate them against market data, validate them against realized outcomes, and build the web applications that put them in front of clients.

This is not a financial analyst, business analyst, data analyst, data scientist, risk analyst, financial modeler, or quant researcher role, and it is not for ad-hoc scripting, spreadsheet modeling, reporting, or research. This is an engineering role that requires a strong foundation in quantitative finance. Most of your time will be spent designing, coding, testing, and shipping production-quality systems end to end — quantitative models, data pipelines, APIs, and client-facing web applications in a cloud environment.

The Quantitative Analyst will:

• Build and maintain cash-flow projection models for private fund interests, including lifecycle event modeling (capital calls, distributions, NAV evolution) and Monte Carlo simulation
• Implement and calibrate discount-rate and fair-value methodology against observed secondary-market transaction data
• Extend coverage to new vehicle types, including evergreen and interval funds with gated or periodic liquidity
• Produce scenario analysis (high / base / low), advance rates, and audit-ready valuation output
• Build and maintain the private-market risk factor model: segment-level historical return series, volatility, beta, correlation, value-at-risk, and concentration analytics
• Implement historical and hypothetical stress scenarios and portfolio what-if analysis
• Develop allocation-versus-limit monitoring and portfolio risk reporting
• Build and maintain the front-end and API layers of the platforms, alongside the models behind them
• Run and improve the quarterly production cycle: data pipelines from public filings and commercial providers, model runs, and the reporting that goes to the board and to clients
• Validate models through champion–challenger testing, backtesting against realized outcomes, and documented methodology reviews
• Document methodology, design decisions, and code so that any team member can pick up any component
• Contribute across the other platforms and internal applications as the team's priorities require

We're looking for someone with:

• A bachelor's degree in financial engineering, quantitative finance, mathematics, statistics, physics, computer science, or a related quantitative field; master's preferred
• Working command of the core toolkit: time value of money, discounted cash flow, NPV and IRR, volatility and correlation estimation, value-at-risk, Monte Carlo simulation
• Solid statistical foundation: regression, time-series analysis, probability distributions, parameter estimation
• Ability to read a methodology paper or a fund document and turn it into a working model
• 1–3 years of professional experience writing production Python, including the scientific stack (NumPy, pandas, SciPy)
• 1–3 years of SQL experience
• Sound object-oriented design and the discipline to write tested, maintainable code
• Comfortable in Git-based workflows
• Docker containerization and AWS cloud

Preferred:

• Master's degree in financial engineering, quantitative finance, or mathematical finance
• Exposure to private markets or alternative assets: fund structures, distribution waterfalls, NAV reporting, secondary transactions
• Experience with factor models, stress-testing frameworks, or model validation
• Familiarity with financial data providers (such as Preqin, PitchBook, Burgiss, and Bloomberg)
• Progress toward CFA, FRM, or CAIA
• Experience building web applications — front end and API layer — ideally in Vue.js/Flask, or in a comparable framework (React, Node.js, Jinja2, etc.)
• CI/CD pipelines (Bitbucket pipelines or similar)
• Experience with AI-assisted development tools (e.g., Claude Code, GitHub Copilot) and LLM APIs
• Linux
• JavaScript
• CSS

Working style:

• Detail-oriented: checks the numbers, tests the edge cases, and doesn't ship something that hasn't been verified
• Dependable: can be handed a piece of work and trusted to deliver it well independently — and raises a flag early when something is at risk. In a small team, everyone has to be someone the rest can count on.
• Collaborative and quick to learn: asks good questions, shares knowledge, reviews others' work, and picks up whatever the team needs
• Takes initiative and ownership: sees what needs doing and does it, owns a component end to end, and documents it so they don't have to be the only one who knows it
• Balances rigor with delivery: cares that the model is right, and also that it ships
• Communicates clearly with both technical and non-technical colleagues

Featured Benefits

• Medical, Dental and Vision Insurance
• Company Paid Life and AD&D Insurance
• Disability Insurance
• 401k Program with Employer Matching

Equal Opportunity

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, disability, sexual orientation, national origin, or any other category protected by law.