Quantitative Analyst

Gazprom Energy
Manchester
2 years ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Data Scientist, Energy Modelling, Hybrid

Performance Data Analyst

Performance Data Analyst

Performance Data Analyst - Part Time

Senior Analyst – Data Science

Data Analyst at jobr.pro

Want to join us on our journey?

We are looking for a Quantitative Analyst to join us on our journey. You will be responsible for designing and implementing pricing models in a collaboration with Front Office and other stakeholders. Working cross asset to design both vanilla and exotic structured derivatives, you will also have exposure to Algorithmic trading to optimise value capture.

What you will do

As a Quantitative Analyst you will be responsible for designing, implementing, and integrating robust quantitative models and tools, for the valuation and risk management of structured derivatives. In addition you will help in maintenance and continuous improvements of the pricing by interrogating the data to understand the market dynamics. By doing this you will be able to capitalise on findings to make informed modelling decisions and develop signals to optimise trading decisions. You will also be responsible for;

  • Design and implement models capturing the key drivers of structured product pricing with a balance between accuracy and complexity
  • Designing and implementing state-of the art pricing models in collaboration with Front Office and other stakeholders
  • Develop Quantitative algorithmic trading strategies
  • Responsible for the thorough testing of models and trading strategies
  • Manage longer term projects alongside with urgent requests arising from the desks

 

What you will bring to the role

A strong mathematical background with a knowledge of option pricing theory and stochastic calculus will be essential for success. Ideally you will also have experience of quant models within a trading environment through a deal lifecycle. In addition you will need;

  • Programming skills (ideally Python)
  • Write code to production quality standards
  • Ability to communicate complex issues in a clear and concise manner
  • Work to tight deadlines in a trading environment
  • Team player who works well with immediate team but also with other stakeholders and users of the pricing library.
  • Excellent written and verbal communication skills
  • Experience on surface volatility modelling a plus
  • Experience in Commodities a plus

About us

Securing Energy for Europe GmbH (SEFE GmbH) is a major European energy company focused on maintaining the security of supply and generating commercial value in Europe. Its main business areas include supplying energy to customers, energy trading, gas transportation and the operation of gas storage facilities. SEFE GmbH is an internationally operating group consisting of around 50 companies in 16 countries in Europe, Asia and North America. The SEFE Group employs approximately 1,500 employees, around 200 of whom work at its Berlin headquarters.

SEFE Marketing & Trading Limited (SM&T) is an integral part of the SEFE  Group. Headquartered in London, SM&T is an agile multi-commodity trader and trading partner. With deep experience in derivatives, digital and analytics and ready for the opportunities arising from the energy transition, we seek to create value, both on a proprietary basis and for its partners, in all key European gas, LNG, power and environmental products markets.

Our culture is defined by our people. Through living our values every day, we continue to create a culture that enables us all to succeed. We work as one team with our customers, our parent company and each other in order to understand each other’s needs. With an unstoppable passion for excellence, growth and learning, we’re committed to creating an environment that fosters the development of knowledge, skills and experience, so that our people can thrive and prosper in their careers with us. We believe that we have the best team in the industry which makes us a trusted partner across international capital and energy markets. Our diverse employee base, with a wealth of expertise, knowledge and experience makes SM&T a truly exciting place to work. We encourage new ideas and initiatives as innovative thinking is central to how we do business. Most importantly, we are a growing and developing business where inspired individuals can make a difference and help shape our future.

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 to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

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

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.

Neurodiversity in Machine Learning Careers: Turning Different Thinking into a Superpower

Machine learning is about more than just models & metrics. It’s about spotting patterns others miss, asking better questions, challenging assumptions & building systems that work reliably in the real world. That makes it a natural home for many neurodivergent people. If you live with ADHD, autism or dyslexia, you may have been told your brain is “too distracted”, “too literal” or “too disorganised” for a technical career. In reality, many of the traits that can make school or traditional offices hard are exactly the traits that make for excellent ML engineers, applied scientists & MLOps specialists. This guide is written for neurodivergent ML job seekers in the UK. We’ll explore: What neurodiversity means in a machine learning context How ADHD, autism & dyslexia strengths map to ML roles Practical workplace adjustments you can ask for under UK law How to talk about neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in ML – & how to turn “different thinking” into a genuine career advantage.