Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Quantitative Strategist - Treasury Strats (m f x)

E Fundresearch
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Credit Quantitative Strategist

AVP/VP, Quantitative Strategist, Equities

Senior Quantitative Strategist

VP, Quantitative Strategy & Data Insights

Cross-Asset Risk Premia Research – Quantitative Strategist – Vice President

Quantitative Research - Credit - Vice President

Overview

Quantitative Strategist - Treasury Strats (m f x) London


Treasury Vollzeit ohne Führungsaufgaben mit betrieblicher Altersvorsorge


Group Strategic Analytics (GSA) is part of Group Chief Operation Office (COO), which acts as the bridge between the Bank's businesses and infrastructure functions to help deliver the Bank's efficiency, control, and transformation goals.


You will join the Treasury Strats team within GSA. The team is responsible for managing liquidity, funding risks, and financial resources across the Bank. They develop systems to price and optimize liquidity, funding risk, and capital measures, and improve risk-adjusted profitability by measuring and presenting liquidity risks to support macro management decisions and exposure management.



  • Develop functionality within the Bank's strategic analytics platform to calculate and optimize portfolio metrics focused on funding and liquidity risk across trading desks and globally.
  • Automate daily funding processes and reporting, improve existing risk processes, and enable appropriate controls.
  • Continuously enhance the existing codebase to efficiently develop new functionalities in a dynamic environment.
  • Collaborate closely with Treasury, Traders, Risk, Finance, and other stakeholders to understand requirements and design strategic solutions.
  • Educated to university degree level or equivalent in a quantitative discipline such as finance, math, physics, computer science, econometrics, statistics, or engineering. Advanced degrees like MSc or PhD are a plus.
  • Excellent programming skills, with experience in the financial services industry; proficiency in Python and C++ is required.
  • Experience working on a Financial Resource Management (FRM) desk and/or optimizing collateral management and funding & liquidity risk costs under business and regulatory constraints.
  • Effective communication skills across multiple teams and functions, along with strong presentation skills.
  • Ability to prioritize tasks under tight deadlines.

We are committed to providing an environment focused on your development and wellbeing, believing a healthy, engaged workforce performs best.


You can expect

  • Hybrid Working model, allowing eligible employees to work remotely part of the time.
  • Competitive salary and non-contributory pension.
  • 30 days' holiday plus bank holidays, with options to purchase additional days.
  • Life Assurance and Private Healthcare for you and your family.
  • Flexible benefits including retail discounts, Bike4Work scheme, and gym benefits.
  • Support for CSR activities, including 2 days' volunteering leave annually.
  • Training and development opportunities to advance your career.
  • Flexible working arrangements to help balance personal priorities.
  • Reasonable adjustments for employees with disabilities, such as assistive equipment.


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.