Quantitative Analyst – Asset Allocation & Portfolio Construction (Buy-Side)

Octavius Finance
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist - GenAI - Consultant

Data Scientist - GenAI - Consultant

Principal Data Scientist

Sr. Data Scientist

Data Scientist, Machine Learning Engineer, Data Analyst, Data Engineer, AI Engineer, Business Intelligence Analyst, Data Architect, Analytics Engineer, Research Data Scientist, Statistician, Quantitative Analyst, ML Ops Engineer, Applied Scientist, Insigh

Data Analyst (Cars Data Science & Analytics) - Manchester, UK

An exciting opportunity has arisen to join a leading quantitative team focused on developing advanced asset allocation models and portfolio construction algorithms. This team covers all major global fixed income markets, including credit, interest rate, and foreign exchange risk, working in close collaboration with portfolio managers, traders, and colleagues across risk management, structured finance, and application development.

 

Key Responsibilities:

  • Develop innovative analytical tools and strategies for asset allocation and portfolio construction.
  • Collaborate with portfolio and risk managers to gather requirements and deliver customized quantitative solutions.
  • Program as part of a quantitative development team, contributing to a library of advanced models.
  • Take on leadership responsibilities, including mentoring junior analysts and driving key projects.

 

Required Skills and Experience:

  • Advanced degree in a quantitative discipline (PhD preferred) such as mathematics, finance, engineering, or a related field.
  • Minimum of 10 years of experience in quantitative research, preferably in fixed income on the buy-side.
  • Strong expertise in statistical techniques, including PCA, optimization methods (linear, quadratic, mixed integer), regression models, and practical machine learning applications.
  • Extensive knowledge of asset allocation methodologies, including mean-variance optimization, scenario-based models, robust allocation techniques, and Black-Litterman frameworks.
  • Proficiency in portfolio construction techniques across macro, sector, and security levels.
  • Programming expertise in Python, C++, and/or Java.
  • Additional experience in structured finance, credit modeling, or Monte Carlo simulations is a plus.
  • Proven ability to lead independent research and work effectively within a collaborative team environment.
  • Excellent communication and presentation skills to convey complex ideas clearly.

 

Apply to

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.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.