

HCLTech
FM&I Modeling Analyst
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an FM&I Modeling Analyst with a contract length of "unknown", offering a pay rate of "unknown". Key skills include expertise in European energy markets, modeling techniques, and coding proficiency in Python. An undergraduate degree in a STEM subject is required.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
November 7, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#ML (Machine Learning) #NumPy #Datasets #Data Engineering #Python #Regression #Libraries #Data Pipeline #Pandas #Data Science #Time Series
Role description
Key Accountabilities:
• Knowledge and experience in global energy markets with the ability to identify and prioritize fundamental and quantitative analysis/modelling that provides commercial insights to the trading organization.
• Strong communication skills with the ability to communicate analytics to necessary stakeholders and influence commercial decisions.
• Develop and implement fundamental balances, pricing models and other tools to surface commercial opportunities within the low-carbon, power, gas, and oil markets, harnessing best practices and advanced modelling techniques.
• Engage with stakeholders (traders and analysts) to ensure that solutions/models are optimal and deliver deep commercial insight.
• Identify repetitive processes that can be standardized into modules that can be reused across projects.
Essential Experience
• Undergraduate degree in STEM subject or quantitative discipline.
• Knowledge of European energy markets (e.g. gas, LNG, or power).
• Understanding of supply and demand drivers together with how physical and related financial instruments are traded.
• Track record of working with traders or other business stakeholders to create commercially actionable models.
• Experience with a range of modelling techniques including, regression, time series analysis, forecast modelling and machine learning.
• Excellent problem-solving skills.
• Experience using a coding language to develop models and analytical tools.
• Experience manipulating and analyzing large, complex datasets.
Desirable Experience & Skills
• Practical knowledge of data engineering practices (designing and building robust data pipelines)
• Knowledge of python and core libraries applicable to data science (e.g., pandas, numpy, statsmodel)
Key Accountabilities:
• Knowledge and experience in global energy markets with the ability to identify and prioritize fundamental and quantitative analysis/modelling that provides commercial insights to the trading organization.
• Strong communication skills with the ability to communicate analytics to necessary stakeholders and influence commercial decisions.
• Develop and implement fundamental balances, pricing models and other tools to surface commercial opportunities within the low-carbon, power, gas, and oil markets, harnessing best practices and advanced modelling techniques.
• Engage with stakeholders (traders and analysts) to ensure that solutions/models are optimal and deliver deep commercial insight.
• Identify repetitive processes that can be standardized into modules that can be reused across projects.
Essential Experience
• Undergraduate degree in STEM subject or quantitative discipline.
• Knowledge of European energy markets (e.g. gas, LNG, or power).
• Understanding of supply and demand drivers together with how physical and related financial instruments are traded.
• Track record of working with traders or other business stakeholders to create commercially actionable models.
• Experience with a range of modelling techniques including, regression, time series analysis, forecast modelling and machine learning.
• Excellent problem-solving skills.
• Experience using a coding language to develop models and analytical tools.
• Experience manipulating and analyzing large, complex datasets.
Desirable Experience & Skills
• Practical knowledge of data engineering practices (designing and building robust data pipelines)
• Knowledge of python and core libraries applicable to data science (e.g., pandas, numpy, statsmodel)






