Company Description
Fastmarkets is an industry-leading price-reporting agency (PRA) and information provider for global commodities, providing price data, news, analytics and events for the agriculture, forest products, metals and mining and new-generation energy markets.
Fastmarkets' data is critical for customers seeking to understand and predict dynamic, sometimes opaque markets, enabling trading and risk management. Fastmarkets is a global business with a history dating back to 1865 and is built on trust and deep market knowledge. It has more than 600 employees spread across global locations in the UK, US, China, India, Singapore, Brazil, Belgium, Finland and beyond.
The Role
The Data Scientist will play a pivotal role in partnering with commodity subject matter experts to develop new forecasts and data sets, while also transforming the company's existing commodity price forecasting and benchmarking capabilities into scalable, efficient solutions using Python, R, and other advanced analytics platforms. This role is central to enhancing the accuracy, agility, efficiency and transparency of forecasting processes. The Data Scientist will also lead the integration of artificial intelligence and machine learning techniques to uncover patterns, improve predictive performance, increase automation and efficiency, and develop new products and tools. By bridging traditional methods with new technologies, the Data Scientist will help drive innovation and operational excellence in a data-driven environment. Key metrics for success in this position include delivering timely updates to forecasting models and data products, ensuring measurable improvements in forecast accuracy for key commodity benchmarks and successfully deploying AI-powered analytics tools with minimal issues, while maintaining strong stakeholder engagement and communication.
The ideal candidate will thrive in a fast-paced and innovative environment, have a can-do attitude, and can juggle a wide variety of tasks and projects.
Principal accountabilities * Modernize Forecasting Models - Support rebuilding and enhancing existing Excel-based commodity price forecasting models using Python, R, or other advanced analytics tools to improve scalability, maintainability, and performance.
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