Senior Quantitative Advisor
Regime Detection & Systematic Equities
The Opportunity
We are an emerging proprietary trading desk building a next-generation Multi-Dimensional Regime Detection Framework (AQS). Our internal development team, led by a capable Lead Researcher, has completed the architectural design and implementation planning for a Python-based system utilising Hidden Markov Models (HMM) and PCA-based correlation analysis to trade S&P 100 equities.
We are seeking a Senior Quantitative Advisor to provide expert validation and mentorship at critical project gates — not to build the system, but to ensure we build it right.
Key Objectives
Mathematical Validation
Review our application of HMM (Gaussian Mixture) and PCA to ensure statistical robustness and prevent overfitting.
Gate Reviews
Conduct Go/No-Go reviews at critical project milestones — specifically the Week 4 "Dimension Review" and Week 7 "Backtest Review".
Risk Governance
Guide the team in establishing SR 11-7 compliant model risk management practices appropriate for an agile startup environment.
Responsibilities
Code & Logic Audit
Review core logic in our volatility.py (HMM implementation) and correlation.py (PCA loading) to ensure they are production-ready.
Backtest Reality Check
Audit backtesting engine assumptions — transaction costs (40bps vs 60bps stressed), look-ahead bias, and regime hysteresis implementation.
Mentorship
Provide high-level guidance to our junior developers and Lead Researcher on live trading gotchas, including market microstructure issues during regime transitions.
Infrastructure Advice
Validate technology stack choices (FastAPI, SQLite for MVP → PostgreSQL) and advise on data ingestion reliability.
Ideal Candidate
10+ years in quantitative finance (Buy-side preference: Hedge Fund, prop desk, or systematic asset manager)
Deep experience with Regime Switching Models, Volatility Targeting, and Signal Processing
Familiarity with Model Risk Management (MRM) best practices and SR 11-7 guidelines
Fluent in Python — scikit-learn, hmmlearn, pandas — able to read and audit production codebase
Interested?
Send us a brief introduction — your background, relevant experience with regime detection or systematic equity models, and any relevant work or publications.
Apply Nowenquiry@alpha-techlab.com