Quant Developer / Researcher (London)

Lazard is one of the world’s preeminent financial advisory and asset management firms. Our people and culture make the difference. While global in presence and reach, ours is a close, collaborativecommunity of just over 3,000 professionals. Lazard is a place of continuous knowledge sharing, skill development and relationship building, where professionals grow and succeed together. Our entrepreneurial culture, flat structure and embrace of individual differences, allow creative ideas, original concepts, and unique perspectives to drive our business forward — and for careers to take flight.

Many of the world’s leading investors — from individuals to institutions across the globe — have entrusted Lazard Asset Management. We pride ourselves in uncovering the best investment opportunities for our clients. The purpose of our asset management business is to help our clients invest for the future — whether it’s for retirement, to grow and preserve inter-generational wealth, or to benefit the organizations that make our world smarter, healthier and more sustainable.

Role Profile:

Lazard Asset Management is seeking a Quant Developer / Researcher to join its Quantitative Research team in London. The role is designed for an exceptional programmer who wants to move into quantitative research. From the outset the position is split roughly evenly between quantitative development and research, and the balance is expected to shift progressively towards research as the successful candidate builds domain depth and a track record of research contribution.

On the engineering side, the immediate priorities are to scale up research code, build agentic AI research pipelines, automate workflows end-to-end, migrate existing R research code to Python, and take research implementations into production. On the research side, the successful candidate will work alongside senior researchers on signal construction, empirical testing, and evaluation, taking on progressively more independent research ownership.

We are looking for a candidate whose programming ability is genuinely exceptional, who has a very good working understanding of modern AI pipelines, and who already brings a solid grasp of capital markets together with some exposure to quantitative research concepts. Just as important is the ambition to become a researcher: this is a development role with a deliberate and supported path into research, not a permanent engineering seat.

Key Responsibilities

Development and AI Pipelines (Initial Focus)

We'll trust you to:

• Scale research code from prototype to robust, production-grade implementations, with close attention to correctness, speed, memory footprint, and reproducibility.

• Design, build, and maintain agentic AI research pipelines — orchestration, tool use, context management, evaluation harnesses, and guardrails — to test and assess research ideas at greater scale.

• Automate research, data, and reporting workflows end-to-end, removing manual steps and operational risk.

• Convert existing R research code to Python, preserving numerical equivalence while improving structure, testing, and maintainability.

• Take research models, backtesting frameworks, and analytics into production, and develop the Python research infrastructure used daily by the team — with proper version control, monitoring, data quality controls, and rising engineering standards for testing, code review, and environment management.

• Evaluate emerging AI models, frameworks, and evaluation methods, and judge which genuinely earn a place in the research stack.

Quantitative Research (Growing Over Time)

We'll trust you to:

• Work with senior researchers to construct and test return-forecasting signals, from initial specification through empirical evaluation — running in-sample, out-of-sample, robustness, and sensitivity checks, and reporting results honestly, including where they are weak.

• Build diagnostics and evaluation frameworks that make research results easier to interrogate and challenge.

• Vet candidate data sources for coverage, quality, point-in-time integrity, and economic rationale before they enter the research process, and document methodology, results, and limitations to the standard expected of a regulated investment process.

• Over time, take ownership of research questions end-to-end: economic rationale, hypothesis formation, data engineering, factor construction, and testing.

You'll need to have:

• Typically three to seven years of professional experience in quantitative development, research engineering, or a closely related technical role, at least part of it in or alongside a financial markets environment.

• Exceptional programming ability. Python to a very high standard, with a demonstrated record of clean, modular, tested, production-quality code, and comfort with profiling and performance optimisation.

• A very good understanding of AI pipelines: LLM-based systems, agentic orchestration, retrieval and context engineering, and the evaluation and testing of non-deterministic components.

• Proven experience taking research or prototype code into production and owning it thereafter.

• Sufficient proficiency in R to read, understand, and faithfully translate existing research code into Python.

• A good understanding of capital markets — instruments, market structure, and the data that describes them.

• Some exposure to quantitative research concepts such as return forecasting, factor models, backtesting, portfolio construction, or statistical inference on financial data.

• Fluency with modern engineering practice, including version control, automated testing, continuous integration, code review, and reproducible environments.

• A clear and credible ambition to move into a research role, supported by evidence of curiosity and analytical rigour.

• Strong collaboration and communication skills in a research-driven environment.

• Deep familiarity with the Python numerical and data stack (NumPy, pandas, SciPy, scikit-learn, and modern columnar tooling such as Polars or DuckDB).

• Hands-on experience with agent frameworks, LLM APIs, and the design of evaluation harnesses for LLM-based systems.

• Experience with performance optimisation, parallelisation, or distributed computing.

• Experience working with large financial datasets, including point-in-time data, vendor feeds, and corporate action handling.

• Prior experience migrating a research codebase between languages, particularly R to Python.

• A postgraduate qualification in a quantitative discipline, or comparable evidence of self-directed research capability.

Personal Characteristics

• Sets an exceptionally high technical bar and takes evident pride in the quality of what is built.

• A logical, structured thinker who reasons from first principles and writes code that reflects that clarity.

• Genuine intellectual ambition — wants to understand and eventually own the investment ideas, not only implement them.

• Strong sense of initiative and ownership; drives projects forward at pace with appropriate guidance from senior team members.

• Healthy scepticism, particularly towards AI-generated output and results that look too good to be true.

• Operates effectively in a collaborative, research-oriented environment while holding a high personal bar for speed and quality.

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