Quant Developer / Researcher (London)

Overview

In this role you will bridge development and research within Lazard Asset Management’s Quantitative Research team in London. You will scale research code, build AI research pipelines, and move research into production, while progressively taking on independent research ownership. The position offers a clear path from engineering toward research, supported by senior colleagues. You will work to test, evaluate, and improve investment signals and models, contributing to a production-grade research stack. This is a high-impact, technically demanding opportunity in a collaborative, research-driven environment.

Responsibilities

• Scale research code from prototype to production-grade implementations emphasizing correctness and performance
• Design and maintain agentic AI research pipelines including orchestration, context management, evaluation harnesses, and guardrails
• Automate research, data, and reporting workflows end-to-end to reduce manual steps and risk
• Migrate R research code to Python with preserved numerical equivalence and improved structure
• Develop Python-based research infrastructure for daily team use with version control and data quality controls
• Evaluate emerging AI models, frameworks, and evaluation methods for inclusion in the research stack
• Growth into Quantitative Research tasks such as constructing and evaluating return-forecasting signals, with robust validation and honest reporting
• Create diagnostics and evaluation frameworks to interrogate research results effectively
• Vet data sources for coverage and integrity and document methodologies, results, and limitations to the standards of a regulated process
• Over time, own research questions end-to-end including hypothesis formation, data engineering, and testing

Key requirements

• Three to seven years of professional experience in quantitative development, research engineering, or similar, in a financial markets environment
• Exceptional programming ability in Python with production-quality, modular, tested code
• Strong understanding of modern AI pipelines (LLMs, agentic orchestration, retrieval/context engineering) and evaluation of non-deterministic components
• Experience taking research or prototype code into production and maintaining it
• Proficiency in R to read and translate existing research into Python
• Solid understanding of capital markets and related data
• Experience with return forecasting, factor models, backtesting, portfolio construction, or statistical inference on financial data
• Fluent in modern engineering practices: version control, automated testing, CI, reproducible environments
• Ambition to move into a research role with curiosity and analytical rigor
• Strong collaboration and communication in a research-driven setting
• Deep familiarity with Python data stack (NumPy, pandas, SciPy, scikit-learn, Polars, DuckDB)
• Hands-on experience with agent frameworks, LLM APIs, and evaluation harnesses for LLM-based systems
• Experience with performance optimization, parallelisation, or distributed computing
• Experience handling large financial datasets and vendor feeds
• Postgraduate qualification in a quantitative discipline (or equivalent evidence)

• Strong collaboration and communication
• Initiative and ownership
• Analytical rigour and logical thinking
• Python (production-quality, profiling, optimization)
• R (read/translate code)
• NumPy

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