Quantitative Strategist

About the Role

We are enhancing an internal platform that transforms diverse and unstructured market data—including sell-side research, central bank communications, news and social media trends, prediction markets, time-series analyses, and market prices—into timely, structured signals specific to a macro trading team. This platform includes features such as research ingestion supported by Large Language Models (LLMs), live market tracking, statistical dislocation reports, lead-lag analyses, and a central bank policy monitoring engine.

Previously built and maintained by the Chief Quant Strategist (also co-COO), we are now seeking a Quantitative Strategist to assist in operating and expanding this platform by taking full ownership of selected workstreams under senior guidance. This hands-on contributor role offers a clear progression path toward broader platform stewardship for the right candidate.

Key Responsibilities

• Manage and enhance quantitative research reports, improving statistical dislocation, lead-lag relationships, and correlation analyses across instruments such as rates, FX, equities, and futures.
• Operate and refine live monitoring tools and briefings, including scheduled news/social media scans, prediction market trackers, and macro updates distributed via collaboration tools and email, focusing on signal clarity and actionability.
• Contribute to development of central bank and research parsing modules, including a multi-bank policy monitor and sell-side research parser.
• Develop and validate data pipelines by integrating approved data sources and designing scheduled jobs that produce reliable reports with built-in sanity checks and reconciliation procedures.
• Create precise, timely deliverables for portfolio managers and traders, such as formatted PDFs, tables, collaboration platform posts, and email alerts designed for easy consumption and action.
• Continuously learn and adopt platform standards regarding data validation, statistical rigor, and robust engineering practices, gradually progressing to designing these standards independently.

Required Qualifications and Skills

• Proficient in Python programming, focusing on writing clean, well-structured, and reliable code for data pipelines, scripting, and analysis with strong practices in error handling and version control.
• Expertise in time-series analysis and strong statistical fundamentals; adept with tools like pandas and numpy, and familiar with metrics such as z-scores, correlations, stationarity testing, and principal component analysis. Deep understanding of statistical pitfalls common in sparse macroeconomic data and designing analyses to mitigate these issues.
• Solid foundation in financial markets, able to engage in meaningful discussions on rates, FX, equities, and futures, with a keen interest to deepen macro trader domain knowledge.
• Experience working with Large Language Models (LLMs) for building or analyzing applications involving data extraction, summarization, or tooling, with a nuanced grasp of their advantages and limitations such as hallucination and drift.
• Comfortable with command-line interfaces, proficient in SQL and Excel, and experienced in handling databases and structured datasets. Familiarity with infrastructure tools like Docker and Postgres is advantageous but not mandatory.
• Demonstrated ownership and rigor: able to manage tasks end-to-end, prioritize correctness due to impact on trading decisions, avoid shortcuts and silent errors, receptive to coaching, and motivated to grow professionally.

Additional Preferred Skills

• Experience in production-grade engineering including service deployment, scheduled jobs, Docker environments, and maintaining low technical debt.
• Hands-on experience creating LLM-driven applications with evaluation, safety guardrails, and audit capabilities; familiarity with local/open-weight models, retrieval-augmented generation (RAG), and embeddings.
• Ability to design frameworks for validation, evaluation, and statistical robustness testing beyond mere application.
• Full-stack or frontend development experience, particularly incorporating LLM-enhanced features that can be verified.
• Exposure to Bloomberg or buy-side market data platforms; knowledge of central bank processes and rate market concepts such as Overnight Indexed Swaps (OIS), meeting-date forwards, and policy pricing.
• Advanced knowledge of derivatives pricing, prediction markets, alternative data sources, and social or news monitoring.
• Familiarity with automation in collaboration tools such as Teams, webhooks, email, and SharePoint/OneDrive for report generation.

Ideal Candidate Profile

• Thrives in small teams with direct responsibility and measurable impact.
• Innately rigorous, preferring to report no signal rather than unreliable findings, particularly cautious with results derived from limited macro data.
• Committed to punctual and accurate briefings; responsive to feedback and quick to improve outputs.
• Eager to build upon an already operational platform, learn established strict standards for data and software quality, and evolve into a role with greater ownership.