About the Team
The Quant Engineering and Risk team builds and runs the company’s alternative asset analytics ecosystem end to end — a set of connected pillar platforms covering the alternative-investment decision lifecycle:
- ValueAlt — market-calibrated fair-value pricing and advance-rate engine for private fund interests
- AltLens — portfolio risk and analytics platform: volatility, beta, value-at-risk, correlation, concentration, and stress testing built on private-market historical returns
- AltSignal — AI-enabled fund due-diligence and screening platform
- AltDeal — buyer-side acquisition underwriting and capital-structure analysis platform
We are a small team that owns these client-facing products end to end — methodology, model implementation, data pipelines, APIs, and the web applications — on a Python, Vue.js, and AWS cloud stack. AI-assisted development is a core part of how we build, and we invest deliberately and heavily in wiki documentation and cross-training so that knowledge is shared rather than siloed.
Position Summary
The Financial 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.
The role may extend across the wider ecosystem — deal underwriting in AltDeal, screening methodology in AltSignal, and the shared services that connect the products. Seniority and compensation depend on qualifications.
What You’ll Do
Pricing — ValueAlt
- 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
Risk — AltLens
- 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
Across the platforms
- 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
What We’re Looking For
Financial engineering and quantitative foundation — required
- 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
Technical — required
- 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
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.
