• Research, prototype, and back test options overlay strategies in Python — covered calls, cash-secured puts, collars, and protective overlays — with realistic assumptions for transaction costs, liquidity, and taxes across SMA accounts • Support PMs across equity and fixed income verticals by designing and applying derivatives-based overlays suited to each asset class • Monitor portfolio-level Greeks, exposures, and risk/return outcomes across many smaller accounts within rules-based risk parameters • Build and maintain research code, data pipelines, and analytics supporting systematic strategy design — signal construction, parameter sweeps, scenario and regime analysis • Translate research into clear, rules-based strategy specifications and playbooks that can be implemented consistently at scale • Evaluate new overlay ideas (income generation, hedging, outcome-oriented strategies) and communicate trade-offs clearly to internal stakeholders • Partner with product managers and engineers to convert manual workflows and research into scalable platform capabilities — strategy engines, trade generation, risk dashboards, monitoring tools • Support daily P&L, risk, and performance monitoring — including exception handling for unusual portfolio events