Quantitative Data Analyst

Lazard•Boston, MA

4d•$90,000 - $150,000•Hybrid

About The Position

The Advantage Quantitative Equity team is hiring a Quantitative Data Analyst to take ownership of the quality, reliability, and usability of the quantitative datasets that power our research and production investment workflows. These datasets are the direct inputs to factor models, risk models, alpha signals, backtests, and live portfolio construction - data quality here has real investment consequences.

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 ICE
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
Strong communication and collaboration skills; effective in small, close-knit teams with direct stakeholder interaction

Nice To Haves

Experience at a quantitative asset manager, systematic hedge fund, or similar investment data environment
Experience supporting 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)
Cloud experience, preferably Azure

Responsibilities

Become a domain owner for key quant datasets (e.g., market data, fundamentals, corporate actions, identifiers/reference data) and develop a detailed understanding of their structure, lineage, known quirks, and intended use in research and production workflows
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 - implemented in code, not spreadsheets
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; collaborate with quant developers to harden and operationalize your solutions
Maintain high-quality dataset documentation and operational runbooks (definitions, assumptions, known quirks, troubleshooting guidance), improving consistency and conventions across the data ecosystem
Maintain datasets over time through routine checks, backfills, and improvements as vendor definitions, schemas, and business requirements evolve
Engage constructively with internal teams and external vendors when addressing data issues or evaluating new sources