Quantitative Researcher at one of the most well-paid multi-strat Quant firms

Saragossa
London
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Postdoctoral Researcher in Biostatistics - Statistical Machine Learning

Senior Postdoctoral Researcher in Biostatistics: Statistical Machine Learning

Machine Learning Scientist

Principal Data Scientist

Looking for a deep learning role that could make the Mariana trench seem like a puddle?



This global investment manager hires asset class experts, such as an ex-portfolio manager from a Tier 1 hedge fund to grow and manage risk.



You’re going to be part of headcount growth this year to over 1000 which includes a 20% increase into research.

You’ll collaborate to develop different machine learning models for trading strategies and create high quality signals.



You'll be working at an MFT systematic multi-strat fund whose performance has been top tier over the last few years, with AUM and headcount growing. Despite their larger size, their structure remains collaborative and not solely pod-shop based, a rare find for an investment firm of this calibre.



You will ideally have prior experience in a fund within financial services with a strong background in machine learning or a related field. Proficiency in Python/R and experience with deep learning frameworks such as PyTorch or TensorFlow will also be required.



Want to join? Get in touch. Salary and total compensation is purely based on performance of your models and the overall business – we can discuss your comp requirements in depth when you get in touch.



No up-to-date CV required.

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.