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Quantitative Developer / Systematic Risk Modeller (Family Office / Asset Management)
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Quantitative Developer / Systematic Risk Modeller
A London-based family office is seeking an expert Quantitative Developer / Systematic Risk Modeller for a focused 3-month contract engagement. Working directly with the principal of the family office, the consultant will design and build a sophisticated statistical risk modelling framework from first principles.
This is a research-oriented, methodical role centred on rigorous econometric modelling using long time series data — monthly and weekly observations spanning 15+ years. The right candidate is someone who finds genuine intellectual satisfaction in getting the statistics right, working with long-horizon data and delivering clean, well-documented Python code. This is not an execution, latency or HFT role. The work is thoughtful, technically demanding and focused on statistical precision.
The Project
The consultant will work across four key deliverables:
- Review and validate an existing 10-equation OLS model
- Design and build a complete residual simulator producing samples based on statistics from 15 years of monthly data
- Model residual distributions with specific focus on: fat and asymmetric tails (individual residuals), covariance structure of residual distributions, 2-regime Markov switching with transition probability estimation, and autocorrelation detection and correction
- Deliver all outputs via a clean, well-documented Python API
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Technical Requirements
- Advanced OLS modelling and residual analysis — including distribution fitting, tail characterisation and simulation
- Extreme Value Theory — Peaks-Over-Threshold (POT), Generalised Pareto Distribution (GPD) and/or GEV for tail modelling
- Copula-based dependence modelling — t-copula or similar for joint tail behaviour across residual distributions
- Regime switching — 2-regime Markov chain with transition probability estimation from data (HMM framework)
- Autocorrelation detection and correction — ACF analysis, Ljung-Box testing, appropriate residual treatment
- Python — production quality, clean, well-documented code (NumPy, Pandas, SciPy, Statsmodels)
- Equities and ETFs (USD and GBP universes)
Ideal Background
We are specifically seeking candidates whose primary experience is in environments where long time series statistical modelling is the core discipline — not high-frequency or execution-focused roles. The ideal candidate will come from one or more of the following backgrounds:
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- Family office — quant modeller or risk researcher working with long-horizon data
- Asset management — quantitative risk, factor modelling, portfolio analytics or return modelling roles
- Pension fund or insurance — quant working with long time series, tail risk and actuarial-style statistical frameworks
- Actuarial background with quantitative finance crossover
- Academic or research background in econometrics or financial mathematics with applied Python delivery experience
Candidates from purely execution-focused, high-frequency or latency-sensitive trading backgrounds are unlikely to be the right fit for this engagement.
What's on Offer
- £700 - £850 per day
- 3-month contract with potential extension
- Direct engagement with the family office principal
- Focused, intellectually stimulating project with clear deliverables
- Remote working
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