Commodity Quant Analyst

Selby Jennings
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Credit Data Scientist

Teaching Associate - Data Science / Statistics

C++ Computer Vision AI Engineer

Senior Data Scientist and Machine Learning Researcher

Data Science Manager - Telematics

Senior Data Scientist

Introduction:

Our client, a Tier 1 Investment Bank are seeking a Commodity Quant Analyst to support their commodity trading business. You will come in as the lead quant support in London. As part of this role you will responsible for the innovative modelling of commodity products and payoffs. You will also work very closely with trading, providing ad-hoc tool building and help systematise their trading strategies. The bank's culture is very collaborative and there is emphasis placed on a good work-life balance.

Key Responsibilities:

  • Develop and implement quantitative models for pricing, risk management, and trading strategies in the commodities markets.
  • Conduct research and development of new analytical frameworks and financial models tailored to commodities.
  • Build and maintain end-user tools in Python, providing user-friendly interfaces for in-house analytics models and machine learning tools.
  • Collaborate closely with traders, risk managers, and other stakeholders to address quantitative modeling issues and support trading activities.
  • Engage with outside organizations to help them comprehend and use front-office analytics models.

Required Skills and Qualifications:

  • An advanced university degree in a STEM subject
  • A minimum of 3 years of industry experience in front office positions is required
  • Previous experience with metals products and models is highly desirable.
  • Proficiency in software development using Python, C#, C++, and/or .NET.
  • Strong communication and interpersonal skills, with the ability to interact effectively with a wide range of stakeholders

How to Apply:Interested candidates are invited to submit their resume with their relevant experience and qualifications

QW1vLlBhd2FyLjU2MjQyLmVmaUBzZWxieWxvbmRvbi5hcGxpdHJhay5jb20.gif

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.