Quantitative Researcher (Systematic Macro)

About the opportunityThe firm is building a systematic, cross-asset macro trading framework that codifies how a discretionary macro trader thinks: identify the handful of real fundamental drivers behind each asset, wait for the market to actually confirm a driver is live and moving, size and act only when independent signals agree, and step aside the moment confirmation breaks down. The research spans regime/risk-environment modelling, catalyst and narrative-confirmation design, statistical trigger construction, and live signal deployment — currently running in production across a core instrument set, against a much wider cross-asset data universe.Execution — coding, backtesting infrastructure, data pipelines, documentation — is increasingly handled by the firm's AI-agent research team. What's missing is a research mind: someone who sets the hypotheses, judges ambiguous evidence, defends or overturns a methodology call, and pushes the framework to its next real breakthrough — not someone needed to write the plumbing.What you'll do- Design and test cross-asset macro signals — which fundamental drivers matter for which assets, under which regimes, and how that mapping should evolve rather than stay static.- Build and defend the firm's house view on time-series methodology: know when a standard estimator (a rolling window, a smoothed correlation) is quietly the wrong tool, and what to use instead.- Run structured, adversarial research — comparative studies between competing signal-construction methods, honest about what's actually proven versus what merely looks good in-sample.- Own signals end-to-end, from idea through out-of-sample testing to live monitoring and the real-time judgment calls a live book demands.- Synthesize multiple independently-researched signal layers into one coherent live strategy, rather than optimizing any single layer in isolation.- Direct and review the AI-driven research pipeline's output — set the standard, catch a subtly wrong result before it ships, and tell the difference between a real finding and a well-dressed one.What we're looking for- Real experience in systematic macro or cross-asset research — someone who thinks in rates, credit, FX, commodities and equity risk together, not a single-asset-class specialist.- A strong, opinionated statistics and time-series background, with the judgment to know when a textbook technique is wrong for the problem in front of you.- A track record of turning a fuzzy, intuition-shaped trading idea into a rigorous, falsifiable research question — and the discipline to kill an idea that doesn't survive testing.- Comfort owning a signal into live production, not just inside a backtest notebook.- Independent judgment. This role sets standards; it doesn't wait for a fully-specified brief.What makes this differentMost quant research roles spend the majority of a researcher's time on implementation. Here, an AI-agent research team already absorbs a large share of the coding, backtesting, and documentation grind, working under the researcher's direction — so the role is weighted toward the highest-value part of research: the hypotheses, the judgment calls, and the moments where the data disagrees with the obvious answer. Candidates who've felt capped by how much of their time goes to plumbing instead of thinking will find that ceiling removed here.Nice to have- Experience with NLP- or LLM-driven signal extraction from unstructured text as a research input.- Experience running or contributing to a live discretionary or systematic macro book.- Comfort directing technical work not personally written day to day.