Forecasting Data Scientist

British Gas

Staines, UK


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The Forecasting Data Scientist will be responsible for defining and implementing the Centrica Forecasting Unit's long and short-term modelling strategy and vision. This is a great opportunity to apply the latest forecasting methodologies to transform the business and benefit millions of customers in a highly operational environment.


  • Develop and maintain state-of-the-art, advanced statistical and machine learning models and algorithms to accurately forecast the electricity and gas demand of millions of Centrica UK Home and UK Business customers
  • Define the long- and short-term modelling strategy & vision with a 3-year roadmap and own its implementation
  • Direct and participate in all relevant industry conversations to provide the Centrica point of view and help improve industry regulations and data to deliver value to our customers
  • Lead development and maintenance of a state-of-the-art forecasting infrastructure and platform to capitalise on big data and the latest forecasting technologies and techniques
  • Guide the integration of demand forecasting platform and data with up-stream and down-stream systems to drive efficiency and automation
  • Develop processes and tools to monitor models' performance, data input/output accuracy and constantly seek ways to increase value to customers
  • Provide on-going maintenance and support of forecasting platform, models, and data to allow for a smooth execution of associated operations
  • Deliver accurate gas forecasts on a shift basis (until full automation is delivered)
  • Source, validate, transform and maintain key forecasting data (such as Meteorological and Smart data) with the final intention to improve quality and accessibility of demand forecasts and enhance the quality of business intelligence provided to the business
  • Actively contribute in the delivery of reliable and accurate analysis to provide additional insights for more accurate hedging, risk and financial decision
  • Work with key stakeholders within and outside the organisation to identify opportunities for leveraging current and new data and modelling techniques to drive optimisation and improvement to demand forecasting, energy pricing and hedging processes
  • Get involved in the induction and training of new members to the team and help share knowledge on technical and process issues


  • Expert in managing large data to extract business value by adopting advanced modelling and visualisation techniques
  • Excellent comprehension of preeminent forecasting methodologies of energy demand
  • Able to handle the raw data loading, package machine learning models, build automated model testing scripts and format data for downstream systems
  • Expert in software development and machine learning model lifecycles
  • Fluent in Python, R and ideally SAS
  • Familiarity with Python's, R's data science libraries and machine learning model lifecycle
  • Expert in relational (e.g. Oracle, SQL Server), non-relational (e.g. Mongo) and cloud-based databases (e.g. DynamoDB, BigQuery)
  • Knowledge of micro-service technologies (docker, kubernetes)
  • Able to work in agile and collaborative working style, with excellent interpersonal skills
  • Able to influence others, collaboratively and constructively with project partners, external stakeholders and internal teams to achieve consensus and positive outcomes
  • Good communication skills, both written and oral, with the ability to translate model outputs into business insight and articulate impact on key business decisions


  • Significant background delivering highly sophisticated forecasting models into production environments and operations
  • Demonstrable experience in building data ETL processes and package advanced Statistical and Machine Learning models in robust, reproducible ways
  • Proven in securing and automating solutions in cloud environments
  • Extensive comprehension of configuring scalable cloud infrastructures and building / maintaining big data pipelines
  • Considerable exposure with a variety of database types, especially with storing big time-series datasets
  • Substantial history in building portals to control and configure platform functionality
  • Exposure to building IaaS and PaaS solutions and big data technologies. e.g. Hadoop, Spark, Kafka
  • Proven exposure to handling large amounts of data, including images
  • Comprehension of working with agile methodologies in matrix teams


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