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Role Profile:
Lazard Asset Management's Advantage Quantitative Equity team is hiring a Senior 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.
This is a hands-on, senior role. You will take over our existing datasets, some of which need care and attention, and make sure they are in standard, consistent forms. You will extend and improve our history (longer coverage, higher frequency) and expand the data we have.
You will also build the automated validation, monitoring, and investigation tooling that keeps our data reliable. The goal is a durable suite of programmatic solutions that lets you and the rest of the team detect, diagnose, and resolve issues quickly.
The role combines data analysis and data engineering. You will be a data specialist who can also model datasets, design and improve tables and schemas, work directly with our databases, and build the pipelines that load and maintain them. You will join our London team and work closely with our team in the Boston and New York offices. The team is small and the work is consequential, with short feedback loops and close collaboration with our quantitative researchers and portfolio managers.
This role requires substantial hands-on experience with quantitative financial data in an investment setting - vendor feeds, time-series datasets, and the data quality problems specific to systematic investing. Candidates without that background are unlikely to be a fit.
We'll trust you to:
Own quant datasets (market data, fundamentals, corporate actions, identifiers/reference data) end to end: their structure, lineage, known quirks, and intended use in research and production. It is on you to make sure the data is fit for use and that data projects keep moving.
Assess the current state of our datasets, prioritise remediation, and make incremental improvements to existing tables and databases, including restructuring them where needed.
Model and design datasets: choose the right grain, keys, and history handling (e.g. slowly changing reference data vs. daily time series, point-in-time / as-known storage), and implement those designs in our databases.
Extend and enrich datasets: longer history, higher frequency (e.g. monthly to daily), additional coverage.
Onboard new datasets end to end: profiling, schema/coverage validation, identifier mapping and normalisation, cross-source reconciliation, documentation, and support for productionisation.
Build and maintain automated data validation and monitoring (completeness, timeliness, duplication, outliers, stale/missing series, mapping breaks), with clear quality metrics and dashboards - implemented in code, not spreadsheets.
Build investigation tools and reusable libraries that let you and the team diagnose data issues quickly and self-serve on data questions.
Investigate data anomalies affecting research or production output: triage, isolate root cause, quantify impact, coordinate remediation, and write the code that prevents recurrence.
Build and maintain data pipelines and DAGs - ingestion, transformation, backfills, and reruns - and collaborate with quant developers to harden and operationalise them.
Act as the primary point of contact with data vendors on issues affecting your datasets; evaluate new sources and vendor changes.
Set and maintain conventions for dataset documentation, quality standards, and operational runbooks across the data ecosystem.
Use AI tools effectively in your work - for coding, investigating issues, and detecting anomalies.
You'll need to have:
Bachelor's degree in a quantitative discipline (e.g., Statistics, Mathematics, Economics, Finance, Computer Science) or equivalent practical experience.
Typically 5+ years of relevant experience, working with quantitative financial data at an investment firm - e.g., a quantitative asset manager, systematic hedge fund, or other buy-side environment.
Hands-on experience with several core dataset types - prices/returns, fundamentals, corporate actions, security master / reference data, factor data, risk model inputs - from at least two major vendors such as Bloomberg, Refinitiv/LSEG, S&P/Compustat, FactSet, MSCI, or ICE.
Solid understanding of the data quality challenges specific to systematic investing: point-in-time correctness, survivorship bias, staleness, partial trading days, identifier changes (CUSIP/ISIN/SEDOL/ticker), and corporate action adjustments.
Experience reconciling across sources and managing vendor schema/definition changes over time.
Strong Python, including pandas and NumPy: able to write clean, maintainable code independently, organise it into reusable libraries used across a codebase, and build validations and automation of real substance. This is not a light scripting role.
Strong SQL: multi-table joins, aggregations, window functions, and CTEs, used to investigate and reconcile discrepancies across large analytical datasets.
Data modeling and database experience: designing schemas and tables for financial time-series and reference data (grain, keys, history/versioning), and working hands-on with relational or analytical databases, including a working sense of performance (indexing, partitioning, query plans).
Strong analytical and debugging judgement; able to diagnose data inconsistencies systematically, prioritise among competing issues, and drive fixes to completion.
Strong communication skills; effective working with a distributed team (London, Boston, and New York) and directly with researchers and vendors.
Desirable Qualifications
Experience with pipelines and orchestration (e.g., Airflow, Prefect, Dagster, or similar) - strongly preferred.
Practical experience applying AI tools to data work (coding assistance, investigation, anomaly detection).
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); deeper query optimisation on large datasets.
Version control (Git), testing, and code review practices.
Cloud experience (we use Azure); familiarity with CI/CD.
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Representation at Lazard
Lazard is an intellectual capital business committed to delivering the best advice and solutions to clients. To achieve these objectives, we focus on attracting, developing and retaining the best talent. We believe that a workforce comprised of people who represent a wide array of backgrounds, experiences and perspectives creates a rich variety of thought that empowers us to challenge conventional wisdom, solve problems creatively and make better decisions.
Lazard was built on the premise that a multicultural firm can best serve a global clientele. As a global firm that has grown organically from local roots in different countries, we have a deep tradition of respecting and appreciating individual differences. Doing so has been core to our success for over 175 years. We are committed to sustaining an environment where every colleague is supported in their professional pursuits, can maximize their individual potential and contribute to our collective success.