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Energy Trading Data Engineer/ Scientist

2 months ago

London, United Kingdom INEOS Energy Full time

Organisational context and job purpose


The INEOS Energy Trading (IET) team forms part of the INEOS Energy group which combines the company’s fully integrated oil and gas exploration and production operations with its clean energy research and development activities.



IET is responsible for trading the gas production from INEOS’ upstream assets in the North Sea and also provides access to market for its internal businesses in countries across Europe with a significant overall energy demand (gas, financial power, carbon).



The primary role of the IET team is to ensure that energy trading and portfolio benefits are optimised for both the upstream and downstream parts of the INEOS business. This role is integral to further strengthening the analytical and trading capabilities of IET with a view to increasing trading activity in existing INEOS energy markets (gas, oil, carbon & LNG). This role will also be central to the aim of IET to maintain the position as a centre of excellence on all energy markets for the INEOS group.



Reporting directly into the Energy Trading Analytics manager, the successful candidate will take a prime role within the Analytics team delivering support to the trading team, senior management, as well the wider INEOS business.




Responsibilities And Accountabilities

  • Help to drive forward AI and machine learning capabilities for Ineos Energy Trading
  • You will be a crucial part of the analytical team, enabling the use of market pricing and fundamental data to help the trading desk analysis for UK and Continental gas, prompt and curve, Carbon, LNG and Oil
  • Helping to build and maintain a comprehensive database (both historical and real-time) and leverage this data by designing and maintaining industry-leading forecasting solutions with a focus on energy commodity price dynamics
  • Supporting the development of energy price forecasting models including AI based trading strategies
  • Analysing and challenging modelling methodologies and suggesting improvements.
  • Working with third party vendors to supply data. Conduct exploratory and investigative analysis on new data sources
  • Building and maintaining a dashboard for analytical and trading team
  • Supporting ad-hoc analysis for trading desk and wider INEOS business



Required Background

  • Bachelor’s or master’s in mathematics, finance, statistics, engineering, computer science or related field
  • Strong analytical skills, programming skills (Python preferred), ability to build/validate models and apply data science & machine learning to operational topics
  • Experience in the energy sector would be beneficial but not essential (Ideally gas, power, carbon)
  • Experience of getting the best results from messy data sets
  • Ideal role for someone with approximately 2 years of experience



Technical Skills

  • Strong Python coding skills
  • Attention to detail / thirst for real answers from data (how, why, what, when)
  • Build / maintain ETL processes to feed IET’s data warehouse from various source APIs
  • Experience with traditional statistical and ML-based methods for time-series modelling
  • Experience in working multiple data types, including time series data
  • Experience with widely used data science toolkits of Python (NumPy, pandas, scikit-learn, TensorFlow, tidyverse, mlr)
  • Data visualization and dashboarding skills (e.g. Power BI, R Shiny)
  • Experience of working on projects within the cloud (e.g. AWS)
  • Experience of data storage platforms (SQL, NoSQL, Map-Reduce frameworks, etc.
  • Experience presenting data visually (Plotly, D3, Tableau)
  • Apply machine learning models / optimization in creative ways to heterogeneous data sets



Behavioural Skills

  • Analytical thinker and a commercial mind set
  • Strong interpersonal skills and the ability to communicate with both business and technical minded colleagues at all levels
  • Versatile worker who enjoys working within a team environment
  • High energy levels and a hands-on approach




Closing date for applications: 9th September 2024