Quantitative Portfolio Manager

Anson McCade
1 year ago
Applications closed

Related Jobs

View all jobs

Data Science & Machine Learning - Senior Associate - Asset Management

Machine Learning Specialist

Senior Credit Data Scientist

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

Senior Data Scientist, Sports

Intraday/Mid Frequency Portfolio Manager - Systematic StrategiesMy client is a multi-manager hedge fund which covers intraday and mid-frequency trading strategies across cash and derivatives markets. The firm is currently looking for PMs trading intraday/mid frequency strategies in Equities or Futures markets to set up their own team in Chicago, New York, London or Paris.They are looking for Quant PMs/Traders with a track record of researching, deploying and managing strategies with Sharpe ratios above 2 to set up teams in return for a significant risk allocation with strong guaranteed compensation, and PnL % payouts once trading goes live.Successful candidates will have experience with statistical analysis/machine learning, and will be skilled in programming languages such as Python and C++, and have their own track record of researching and deploying profitable strategies.The Role:Building a team of Quant Researchers and Traders or building out as a standalone PM.Designing, backtesting, and deploying trading strategies, monitoring and and optimising them over time.Requirements:A Master or PhD level degree from a prestigious university in a numerate field. Previous successful candidates have degrees in Engineering, Physics, Mathematics, Computer Science, etc.Coding proficiency in at least one language, such as C++ or Python.At least three years of experience as a Quantitative Researcher, where you used sophisticated data science methods for the research and optimisation of strategies, with Sharpe ratios of 2+

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.