UK Fund Hiring Entry Level Quant Analysts - Systematic Equity Team

Eka Finance
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Climate Data Scientist

Data science programme lead

Lead Machine Learning Engineer, AI

T Posted byRecruiterLeading UK Fund are hiring 3 entry level PhD quant analysts to work in their systematic equity quant trading team.

Role:-

Initially you will be mentored by a senior member of the team and will be responsible for implementing and optimizing existing strategies. You will work on the research, design and C++ implementation of innovative data analysis algorithms and tools and the research, back-testing, C++ implementation and deployment of new trading strategies.

Requirements:-

PhD from a top tier University in any of the following subjects;puter Science, Machine Learning, Artificial Intelligence, Statistics, Operations Research, Econometrics, Signal Processing,puter Vision.

They will also consider exceptional Masters level students.

An understanding of how to translate your research expertise to contribute to the development and optimisation of quantitatively driven strategies and trading.


Experience of working with large data sets, or noisy data.

 
A distinguished background in research or internships at reputable organisations.


Strong software programming skills in C++ ,Perl or Python.

Demonstrable interest in systematic trading.

A background in time series analysis, statistics, reinforced learning algorithms, portfolio theory.

They are happy to consider candidates who havepleted their PhD this year as well as candidates who graduate in 2018 and are looking for a role on pletion of their PhD .

Interviews will consist of meetings with the senior partners as well as technical rounds with the quants and developers.

The environment is excellent and turnover is incredibly low.

No work visa can be provided for this role.

Job ID TK

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.