Python Developer

Hedge Fund
London
11 months ago
Applications closed

Related Jobs

View all jobs

Python Developer, Data Science, CRM solutions, Financial Services Firm

Python Developer, Data Science, CRM solutions, Financial Services Firm

Python Developer and Machine Learning Specialist: Visa Sponsorship Available

Machine Learning Engineer

Lead MLOPS Engineer

Senior Data Scientist

Job Description

About Us:

We are one of the world’s leading commodities trading houses, leveraging sophisticated technology to trade a broad range of energy, metals, and agricultural commodities globally. Our success is driven by cutting-edge analytics, high-frequency data processing, and a commitment to innovation. We are now looking for a Senior Python Engineer to join our dynamic team in London and drive our next wave of technological advancements.


Role Overview:

As a Senior Python Engineer, you will be at the heart of our technology platform, building robust systems that enhance our trading strategies, risk management, and data analysis. You will collaborate closely with quants, data scientists, and traders, providing Python-based solutions that directly impact the profitability of the business. This is an exciting opportunity to work in a fast-paced, high-performance environment where you can push the boundaries of technology in financial markets.


Key Responsibilities:

  • Design, develop, and maintain highly efficient Python-based applications for commodities trading, risk management, and real-time data analysis.
  • Build scalable, high-performance data pipelines to process large datasets from multiple sources, including real-time market data and fundamental data.
  • Implement robust APIs and microservices to integrate trading systems, analytics platforms, and external data pro...

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.