Quantitative Developer

Glocomms; Greater London; On-site; GBP 60,000 - 85,000; Full time

Job summary

A leading financial institution in London is looking for a skilled Credit Quant Developer to manage the migration of risk model code from R to Python. This role requires strong programming skills, a solid foundation in econometrics, and the ability to solve complex modeling challenges. Candidates should have experience in the banking sector, with a focus on developing efficient, organized code. Collaborating with a dedicated team, you will ensure that all model code is well-documented and production-ready, enabling adaptability to changing regulatory standards.

Qualifications

• Strong programming ability in Python with version control experience.
• Understanding of econometrics and time-series analysis.
• Banking experience, ideally in a regulated environment.

Responsibilities

• Lead migration of quantitative model code from R to Python.
• Develop scalable code structures for evolving needs.
• Handle model challenges with econometric knowledge.
• Collaborate within the Credit QA team for testing and documentation.

Skills

Programming in Python

Problem solving

Econometrics

Time-series methodologies

Version control (GIT)

Tools

R / R-Studio

C++

Job description

Location: London, Hybrid

About the Company

Our client is a prominent global financial institution operating across multiple markets, offering investment banking, corporate banking and risk management services. Known for its strong regulatory discipline and commitment to innovation, the firm maintains robust infrastructure and technology teams that support critical quantitative, risk and analytics functions across its international operations.

Job Description

The Quantitative Analytics team within the investment banking division is seeking a skilled and motivated Credit Quant Developer to support a high‑priority migration of risk model code. This role focuses on quantitative risk (Market, Credit and Interest Rate Risk) rather than front‑office pricing, and plays a vital part in enhancing the stability and adaptability of the firm's credit banking book models.

You will be responsible for translating and restructuring existing model components, primarily migrating code from R to Python - ensuring the newly implemented versions are efficient, well‑organised and maintainable. This role requires strong technical ability, excellent problem solving skills and a solid foundation in econometrics and time‑series methodologies.

Key Responsibilities:

• Lead the migration and translation of quantitative model code, focusing heavily on transitioning existing R models into Python.
• Develop and implement clean, logical and scalable code structures that can be easily adapted to evolving business and regulatory needs.
• Apply strong econometric knowledge to handle model challenges, identifying improved coding solutions when direct translation is not suitable.
• Work collaboratively within the Credit QA team to produce well‑documented, thoroughly tested and production‑ready model code.

Ideal Qualifications

• Strong programming ability in Python, including experience using GIT or similar version‑control tools.
• Solid understanding of econometrics, including foundational concepts such as linear regression, time‑series analysis and factor analysis.
• Experience working within the banking or broader financial services sector, ideally within a regulated environment
• A critical, analytical mindset with the ability to review and challenge model behaviour and code quality.

Nice to have

• Familiarity with R / R‑Studio or the ability to comfortably interpret R code, which supports the migration process.
• Understanding of C++ for potential cross‑team collaboration.
• Exposure to quantitative risk domains including Market Risk, Credit Risk or Interest Rate Risk.
• Advanced econometric knowledge, such as experience with capital models, meta‑models or correlation modelling.