Quantitative Data Analyst

Lazard; Boston (KY); On-site; USD 90,000 - 150,000; Full time

Benefits offered by this job

Health, dental, and vision insurance

Retirement plans

Workplace wellness programs

Job summary

Lazard is seeking a Quantitative Data Analyst to enhance the quality and usability of quantitative datasets for research and production workflows. The ideal candidate will have strong analytical and SQL skills and experience with financial datasets.

Responsibilities include developing automated data validation processes and addressing data issues. The position offers a competitive salary range of $90,000 – $150,000 USD and comprehensive benefits.

Qualifications

• Hands-on experience with quantitative financial datasets.
• Solid understanding of time-series data quality challenges.
• Strong communication and collaboration skills.

Responsibilities

• Become a domain owner for key quant datasets.
• Build and maintain automated data validation processes.
• Investigate data anomalies impacting research outputs.

Skills

Quantitative analysis

SQL

Python

Data validation

Analytical mindset

Education

Bachelor’s degree in a quantitative discipline

Tools

Bloomberg

Refinitiv

Snowflake

Job description

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.

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.

Qualifications

• 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—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; comfortable 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.

Preferred Qualifications

• 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.

Benefits

We offer a highly competitive total rewards package, including a base salary range of $90,000 – $150,000 USD and comprehensive benefits such as health, dental, vision, retirement plans, and workplace wellness programs. Additional incentives may be provided based on performance.