Joshua Zayne

• Engineered and monitored a nightly Python audit & risk pipeline that ingests 60 k+ records in

• Managed trades of a $50 million, multi-asset portfolio, setting strategic allocations, risk limits, and performance targets to consistently deliver superior risk
adjusted returns.
• Provided institutional like insight and leadership, translating complex model output into tradeable themes for external mandates, mentoring a small quant
team of 4VPs and 4 Analyst, and scaling client AUM through disciplined research, trades, and rapid, reproducible algorithms.
• Cultivated and managed relationships with private equity firms, hedge funds, and banks, collaborating across $14 billion assets under control to tailor
bespoke investment solutions and expand institutional mandates.
• Oversaw quant model governance, establishing peer-review standards and statistical validation protocols (e.g. back‐test p-values, IR metrics) to ensure all
factor and risk models met enterprise‐grade robustness criteria.
• Championed multi‐objective optimization projects, integrating mean‐variance criteria with downside‐risk constraints via Lagrangian multipliers and teaching
the team to solve using sequential quadratic programming.

• Designed systematic, multi-asset strategies: from factor-driven equity screens to volatility-weighted futures programs, that consistently delivered strong risk
adjusted returns while keeping drawdowns within tight policy limits.
• Oversaw enterprise-level risk and hedging by integrating tail-risk forecasts, delta-gamma neutral option overlays, and macro-sensitive yield-curve models,
materially lowering portfolio CVaR without sacrificing upside.
• Engineered a martingale-driven Markov-chain Monte Carlo engine that spins up more than half a million synthetic price paths each night, channels the
resulting tail-loss probabilities into pre-market stress dashboards, boosting VaR accuracy by roughly twenty-five percent, and then scanned for tickers with
high likelihood of profitable wins based off previous closes, and presented that in a morning dashboard.
• Architected high-dimensional factor models by combining principal component analysis with L1-regularized regression techniques to distill hundreds of raw
market variables into a concise set of robust predictors. This framework eliminated noise and collinearity, revealing true latent drivers: such as volatility
regimes, seasonality patterns, and cross-asset momentum signals, across equities, commodities, forex and rates. By focusing on a parsimonious factor set,
we improved out-of-sample signal stability by over 20%, reduced model overfitting risk, and accelerated factor-score generation to sub-second latency,
enabling seamless integration into live systematic strategies.

• Provided direct leadership, mentorship, and performance management for 170+ multidisciplinary personnel across multiple duty stations, fostering a culture
of continuous improvement and professional development that directly contributed to enhanced team cohesion and operational readiness.
• Championed and implemented innovative, data-driven process improvements within outpatient services by integrating predictive analytics and visualization
tools to overhaul clinical workflows, significantly enhancing patient safety (98% medication error reduction) and operational efficiency.
• Ensured operational excellence and adherence to rigorous care standards across outpatient mental health services by meticulously managing daily
operations, overseeing resource deployment, and spearheading quality assurance initiatives that improved patient outcomes by 28% and set benchmarks
for regional facilities.

• Authored the mathematical blueprint for automated trading and hedging across asset classes, leveraging live volatility forecasts, and utility-driven allocation
rules, and optimized contract-roll procedures to reinforce portfolio durability and drive superior risk-adjusted returns.
• Developed the quantitative backbone of the research engine, architecting mathematical models and implementing SATA/Python/VBA data pipelines, modular
back-testing frameworks, and advanced factor-screening algorithms like cross sectional z-score thresholding and Informational Coefficient ranking to
transform raw market feeds into rigorously validated trade signals.
• Delivered real-time decision support with automated VaR/CVaR dashboards and option-pricing/hedge-evaluation tools, giving portfolio managers fast,
disciplined insight into risk and performance.