Medium Frequency Quant Trader/ London

Eka Finance
London
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Machine Learning Engineer (d/f/m)

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 Machine Learning Engineer

Data Scientist

Product Manager AI (AI & Machine Learning)

T Posted byRecruiterLeading fund are hiring a medium frequency quant trader to be based in London .

Role:-

You will join a small, prestigious mid-frequency systematic quant team . You will take ownership of the full lifecycle of creating market leading systematic trading strategies. This will consist of researching alpha signals, building state of the art machine learning models and implementation of strategies .

Requirements:-

You should have at least 3 years of experience working within finance, preferably in a buy sidepany, working on alpha research and signal generation working with strategies with a holding period of minutes to hours/ intraday.

Product experience in any asset class will be considered.

Prior exposure to medium-frequency or daily/intraday trading strategies in North America, Europe or Asia would be highly relevant.

Strong grasp on end-to-end strategy development

Proven success developing strategies in the HFT or Intraday space

Masters or PhD degree in a quantitative subject such asputer Science, Applied Mathematics, Statistics, or related field

This firm believes in a work life balance so candidates who are passionate about quant trading and also happy to maintain a work life balance and an ethos of working in a collaborative environment should apply.

Apply:-

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.