AVP – Python Quant Developer – Risk

Barclay Simpson; City Of London; Hybrid; GBP 60,000 - 80,000; Full time

Job summary

A financial services consulting firm in the City of London seeks a Python Model Developer to contribute to the development and automation of quantitative risk models. The ideal candidate will have around 3 years of experience in Python and SQL, along with an understanding of risk domains such as credit or market. This role offers a hybrid working setup with three days in the office, fostering collaboration and continuous improvement.

Qualifications

• Approximately 3 years of experience with Python in a data or risk/quant adjacent role.
• Solid SQL skills for data wrangling and reconciliation.
• Practical Git experience, familiarity with GitLab or similar CI/CD.

Responsibilities

• Contribute to the design, development, and deployment of Python-based risk models.
• Refactor existing code paths and add unit tests.
• Automate recurring processes and collaborate with commercial teams.

Skills

Python programming

SQL for data wrangling

Source control (Git/GitLab)

Risk domain understanding

Tools

pandas

NumPy

GitLab

MongoDB

GCP

Looker

Vertex AI

Job description

About the team

You’ll join a small, London based Financial Risk team that designs, develops and deploys risk models covering credit, market, capital and liquidity for derivative trading. The group works closely with Model Validation, Regulatory Capital, Finance, Treasury, Credit Operations, Enterprise Data and Technology. The environment is cross-functional, commercially focused, and hybrid (typically three days on site).

Role purpose

Help build, maintain and productionise quantitative risk models and the surrounding automation so the business can act quickly on high-quality risk insights. You’ll contribute code, testing, documentation and operational run-books, and support incremental migrations toward cloud tooling.

What you’ll do

• Contribute to the design, development and deployment of Python-based risk models (e.g., components of VaR/ES or PD/LGD pipelines) under senior guidance.

• Refactor and harden existing code paths; add unit tests, data validation checks and logging.

  Build and maintain CI/CD jobs in GitLab for model rebuilds and scheduled tasks; assist with release notes and rollbacks.

  Automate recurring processes and controls (data loads, reconciliations, report generation).

  Collaborate with first-line commercial teams to clarify requirements and triage model output questions.

• Support early cloud migration tasks (e.g., packaging jobs, testing connectors, basic Looker dashboards) with mentorship from seniors.

Tech you’ll use

• Python (pandas, NumPy; matplotlib for basic plots) and SQL for model development and analysis.

• GitLab for source control and deployment pipelines.

• Exposure to MongoDB and GCP services; growing use of Looker and Vertex AI as the stack transitions.

What you’ll learn/exposure

• Industry-standard risk modelling concepts (stress testing, VaR/ES, PD/LGD).

• Model lifecycle & controls (documentation, validation, monitoring, and auditability).

• Stakeholder engagement across risk, finance, treasury, capital & tech.

Minimum requirements (aimed at ~3 years’ experience)

• ~3 years with Python in a data or risk/quant adjacent role, including pandas/NumPy and writing testable, readable code.

• Solid SQL for data wrangling and reconciliation.

• Practical Git experience; familiarity with GitLab or similar CI/CD.

• Understanding of at least one risk domain (market, credit, capital or liquidity) and basic knowledge of common models (e.g., VaR/ES or PD/LGD).

• Comfortable working in a hybrid, collaborative setup with clear, concise communication.

Nice-to-have

• Experience in a bank/consultancy risk team or adjacent regulated environment.

  Python Model Developer

• Exposure to MongoDB, GCP, Looker, or Vertex AI.

  Python Model Developer

• Familiarity with model monitoring/alerting and data quality frameworks.

Ways of working & culture

We value ownership, pace, client focus, and raising the bar while staying collaborative and inclusive. Team members are encouraged to think big, automate where possible, and ship improvements continuously. Hybrid working with three days in the office supports collaboration and learning.