Senior Quantitative Data Analyst

Overview

In this role you will own and improve the quantitative datasets that underpin research, risk, and live portfolio construction. You will work within the Advantage Quantitative Equity team in London, collaborating with colleagues across the US to raise data quality and coverage. You will build automated validation, monitoring, and tooling to detect and fix data issues, enabling faster, more reliable investment workflows. This is a hands-on, data-centric position with a clear impact on investment decisions and research outcomes. You will use AI tools to enhance data work and governance.

Responsibilities

• Own end-to-end quant datasets (market data, fundamentals, corporate actions, reference data) including structure, lineage, and use in research/production
• Assess dataset health, prioritize remediation, and improve tables/databases; restructure when needed
• Model and design datasets (grain, keys, history handling; PIT/known state) and implement in databases
• Extend datasets with longer history and higher frequency; onboard new datasets end-to-end
• Build automated data validation/monitoring with quality metrics and dashboards (code-based)
• Develop reusable investigation tools and libraries for rapid data issue diagnosis
• Triage and remediate data anomalies affecting research/production; quantify impact and prevent recurrence
• Build and maintain data pipelines and DAGs for ingestion/transformation/backfills; collaborate with quant developers
• Serve as primary vendor liaison; evaluate new sources and vendor changes
• Set and maintain conventions for dataset documentation, quality standards, and runbooks
• Leverage AI tools to assist in coding, investigation, and anomaly detection

Key requirements

• Bachelor’s degree in a quantitative discipline or equivalent experience
• Typically 5+ years of relevant experience with quantitative financial data at an investment firm
• Hands-on experience with datasets: prices/returns, fundamentals, corporate actions, reference data, factor data from major vendors (Bloomberg, Refinitiv/LSEG, S&P/Compustat, FactSet, MSCI, ICE)
• Strong Python (pandas, NumPy) and clean, reusable coding practices
• Strong SQL for multi-table joins, aggregations, window functions, and reconciliations
• Data modeling and database experience for financial time-series and reference data
• Strong analytical and debugging skills; ability to diagnose data issues and drive fixes
• Strong communication skills and experience working with distributed teams (London/Boston/New York)

• Collaborative mindset
• Problem-solving and analytical rigor
• Effective communication
• Python (pandas, NumPy)
• SQL with complex queries
• Data modeling and database design (time-series, reference data)

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