Quant Analyst - Treasury

Bruin Financial

Greater London

On-site

GBP 120,000 - 180,000

Full time

14 days+

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Job summary

The role demands a strong academic background, advanced Python skills and experience modelling in financial services, with exposure to ALM, risk methodologies and regulated environments. You will contribute to governance, testing, and production support while communicating insights to senior stakeholders.

Qualifications

  • Master's or PhD in a quantitative discipline.
  • Proven experience in quantitative modelling within financial services.
  • Advanced Python programming skills.
  • Strong understanding of financial mathematics, cash-flow modelling and fixed income products.
  • Experience working with large datasets and complex analytical problems.
  • Excellent communication skills with the ability to engage both technical and non-technical stakeholders.

Responsibilities

  • Develop and maintain quantitative models supporting Treasury risk management and forecasting.
  • Build and enhance Python-based analytical tools and model frameworks.
  • Analyse and validate large, complex financial datasets.
  • Support regulatory and internal risk management exercises.
  • Collaborate with Risk, Finance, Treasury and Technology teams to deliver high-quality modelling solutions.
  • Contribute to model governance, testing, documentation and production support activities.

Skills

Python
Quantitative modelling
Data analysis
Stakeholder engagement
Financial mathematics
Communication

Education

Master’s or PhD in quantitative discipline

Tools

Python

Job description

VP Quantitative Analyst – Treasury

We are partnering with a leading global financial institution to appoint a VP Quantitative Analyst to join a high‑performing Treasury Quantitative Analytics team. This role offers an opportunity to develop and enhance quantitative models that support Treasury activities across liquidity risk, balance sheet management, collateral forecasting, and hedge accounting. Working closely with Treasury, Risk, Finance, and Technology stakeholders, you will play a key role in delivering robust analytical solutions within a highly regulated environment.

Key Responsibilities
  • Develop and maintain quantitative models supporting Treasury risk management and forecasting.
  • Build and enhance Python-based analytical tools and model frameworks.
  • Analyse and validate large, complex financial datasets.
  • Support regulatory and internal risk management exercises.
  • Collaborate with Risk, Finance, Treasury and Technology teams to deliver high-quality modelling solutions.
  • Contribute to model governance, testing, documentation and production support activities.
Requirements
  • Strong quantitative background with a Master’s degree or PhD in a quantitative discipline.
  • Proven experience in quantitative modelling within financial services.
  • Advanced Python programming skills.
  • Strong understanding of financial mathematics, cash‑flow modelling and fixed income products.
  • Experience working with large datasets and complex analytical problems.
  • Excellent communication skills with the ability to engage both technical and non‑technical stakeholders.
Highly Desirable
  • Treasury experience covering Asset & Liability Management (ALM), Liquidity Risk, Collateral Management, Hedge Accounting, IRRBB, ICAAP or related regulatory frameworks.
  • Knowledge of balance sheet modelling and Treasury risk methodologies.
  • Experience operating within a controlled or regulated model environment.
What’s on Offer
  • Opportunity to join a market‑leading quantitative analytics function.
  • Exposure to high‑impact Treasury initiatives and strategic regulatory programmes.
  • Collaborative environment working alongside senior stakeholders across Treasury, Risk and Finance.
  • Excellent career progression within a sophisticated quantitative modelling team.

If you are a quantitative analyst with strong Python development skills and Treasury exposure looking for your next VP‑level opportunity, we’d be keen to hear from you.

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