Commodity Quant Analyst

Selby Jennings
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Credit Data Scientist

Population Health Data Scientist — Remote Contract

Senior DataOps Engineer

Staff Data Scientist

Data Scientist - Men's First Team

Post-Doctoral Research Associate

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.