FM&I Modeling Analyst

  • Houston, TX
  • Posted 1 day ago | Updated 1 day ago

Overview

Hybrid
Depends on Experience
Contract - W2
Contract - 12 Month(s)
No Travel Required

Skills

NumPy
Pandas
Pricing
Problem Solving
Python
Modeling
Quantitative Analysis
Data Science
Energy
Analytical Skill
Analytics
Communication
Conflict Resolution
Data Engineering
FM
Finance
Forecasting
Machine Learning (ML)
Regression Analysis
Time Series
Trading

Job Details

Job Details:

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)

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