Senior Quantitative Data Analyst

Overview

As a Senior Quantitative Data Analyst in Lazard Asset Management, you own the quality, reliability, and usability of quantitative datasets that power research and live investment workflows. You will extend and improve data history, expand coverage, and build automated validation and monitoring tools to keep datasets trustworthy. The role blends data analysis and data engineering, working closely with researchers and portfolio managers across London, Boston, and New York. You contribute to robust data foundations that support factor models, risk models, and live portfolio construction, with a focus on scalable, reproducible solutions.

Responsibilities

• Own end-to-end quant datasets (market data, fundamentals, corporate actions, reference data) including structure, lineage, and usage for research and production
• Assess datasets, prioritise remediation, and incrementally improve tables and databases
• Model and design datasets (grain, keys, history handling) and implement designs
• Extend datasets with longer history and higher frequency
• Onboard new datasets end to end (profiling, validation, mapping, normalization, documentation)
• Build and maintain automated data validation and monitoring with dashboards and quality metrics
• Create investigation tools and reusable libraries to diagnose data issues
• Investigate data anomalies, triage root causes, quantify impact, and coordinate remediation
• Develop data pipelines and DAGs for ingestion, transformation, backfills, and reruns
• Act as primary contact with data vendors; evaluate new sources and changes
• Set conventions for dataset documentation, quality standards, and runbooks
• Use AI tools effectively for coding, investigation, and anomaly detection
• Collaborate with quantitative researchers, portfolio managers, and vendors
• Support production data pipelines and incident workflows
• Familiarity with modern data warehouses and analytical engines
• Apply strong coding, testing, and code review practices
• CI/CD and cloud experience (Azure)

Key requirements

• Bachelor’s degree in a quantitative discipline or equivalent practical experience
• Typically 5+ years of relevant experience with quantitative financial data at an investment firm
• Hands-on experience with multiple core dataset types from major vendors (e.g., Bloomberg, Refinitiv/LSEG, S&P/Compustat, FactSet, MSCI, ICE)
• Solid understanding of data quality challenges in systematic investing (point-in-time, survivorship bias, staleness, corporate action adjustments)
• Experience reconciling data across sources and managing vendor schema changes
• Strong Python (pandas, NumPy) and clean, reusable code
• Strong SQL skills (multi-table joins, aggregations, window functions, CTEs)
• Data modeling and database design for financial time-series and reference data
• Strong analytical and debugging judgement; ability to diagnose inconsistencies and drive fixes
• Strong communication skills and distributed-team collaboration (London, Boston, New York)

• excellent communication
• problem-solving mindset
• cross-team collaboration
• Python (pandas, NumPy)
• SQL
• data modeling and database design

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