FM&I Modeling Analyst

Expired on: Jan 20, 2026

Job Reference: 251023001
Type : Contract / Hybrid
Salary: Best as per market rates
Location: London
Experience : 15 years

Job Description
Are you passionate about energy markets and driven by data? We’re looking for a sharp, commercially minded FM&I Modelling Analyst to join our dynamic team and help shape the future of global energy trading.

About the Role
As an FM&I Modelling Analyst, you’ll play a pivotal role in delivering quantitative and fundamental insights that drive commercial decisions across low-carbon, power, gas, and oil markets. You’ll collaborate closely with traders and analysts to develop models and tools that uncover opportunities and optimize trading strategies.

Key Responsibilities

  • Conduct in-depth fundamental and quantitative analysis of global energy markets to generate actionable commercial insights.
  • Build and maintain pricing models, balance frameworks, and analytical tools using advanced modelling techniques.
  • Translate complex analytics into clear, impactful narratives for stakeholders, influencing trading decisions.
  • Partner with traders and analysts to ensure models are fit-for-purpose and deliver deep commercial value.
  • Identify and modularize repetitive processes to enhance efficiency and scalability across projects.

Essential Qualifications & Experience

  • A degree in a STEM or quantitative discipline.
  • Solid understanding of European energy markets (e.g., gas, LNG, power).
  • Strong grasp of supply-demand dynamics and the trading of physical and financial instruments.
  • Proven experience developing commercially actionable models in collaboration with trading teams.
  • Proficiency in modelling techniques such as regression, time series analysis, forecasting, and machine learning.
  • Skilled in coding (e.g., Python) and handling large, complex datasets.
  • Excellent problem-solving and communication skills.

Desirable Skills

  • Hands-on experience with data engineering and building robust data pipelines.
  • Familiarity with Python libraries like pandas, numpy, and statsmodels.
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