Quantitative Data Analyst

About the role

The Advantage Quantitative Equity team at Lazard is seeking a Quantitative Data Analyst to ensure the quality, reliability, and usability of quantitative datasets that power our research and production investment workflows. This role involves becoming a domain owner for key quant datasets, developing a deep understanding of their structure, lineage, quirks, and intended use, and maintaining these datasets over time.

Responsibilities

• Become a domain owner for key quant datasets (e.g., market data, fundamentals, corporate actions, identifiers/reference data)
• Onboard new datasets end-to-end: profiling, schema/coverage validation, identifier mapping, cross-source reconciliation, documentation, and support for productionization
• Build and maintain automated data validation and monitoring processes (completeness, timeliness, duplication, outliers, stale/missing series, mapping breaks), along with clear quality metrics and dashboards
• Investigate data anomalies impacting research or production output: triage, isolate root cause, quantify impact, coordinate remediation, and write the code that prevents recurrence
• Write Python scripts, pipelines, and utilities (using pandas, NumPy, and related libraries) to automate validation, onboarding, reconciliation, and monitoring workflows
• Maintain high-quality dataset documentation and operational runbooks (definitions, assumptions, known quirks, troubleshooting guidance)
• Support production data pipelines and incident workflows (monitoring, alerts, runbooks, operational readiness)
• Familiarity with modern data warehouses (e.g., Snowflake) and/or analytical engines (e.g., DuckDB, Polars)

Requirements

• Bachelor’s degree in a quantitative discipline (e.g., Statistics, Mathematics, Economics, Finance, Computer Science) or equivalent practical experience
• Hands-on experience working with quantitative financial datasets - e.g., prices/returns, fundamentals, corporate actions, security master / reference data, factor data, risk model inputs - from vendors such as Bloomberg, Refinitiv/LSEG, Compustat, FactSet, or ICES
• Solid understanding of common time-series data quality challenges in a systematic investment context: staleness, point-in-time correctness, survivorship bias, partial trading days, identifier changes (CUSIP/ISIN/ticker), and corporate action adjustments
• Experience working with vendor datasets; comfort reconciling across sources and managing schema/definition changes over time
• Strong SQL skills: ability to write and optimize queries to validate, reconcile, and investigate issues across large analytical datasets
• Strong Python skills: able to write clean, maintainable scripts, pipelines, and reusable utilities independently; comfortable with pandas, NumPy, file I/O, and scheduling
• Strong analytical and debugging mindset; able to diagnose data inconsistencies systematically and drive fixes through to completion
• Effective communication and collaboration skills; effective in small, close-knit teams with direct stakeholder interaction

Qualifications

• Experience at a quantitative asset manager, systematic hedge fund, or similar investment data environment
• Familiarity with modern data warehouses (e.g., Snowflake) and/or analytical engines (e.g., DuckDB, Polars)
• Cloud experience, preferably Azure

Benefits

We offer a comprehensive benefits package designed to enhance the total health and well-being of our employees. This includes competitive base salaries ranging from $90,000 to $150,000 USD, depending on experience and qualifications. Base salary is one component of our compensation package, which also includes comprehensive benefits and may include incentive compensation.

Pay

$90,000 - $150,000 USD

Schedule

Full-time