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- Collaborating closely with cross-functional teams, including data engineers and pricing specialists, to advance our analytics capabilities.
- Contributing to the broader data and analytics community within our organisation, sharing knowledge and driving continuous improvement.
- Leading initiatives to integrate various data science techniques into our underwriting processes, particularly within the Major Property team.
- Strong programming skills in languages like R, Python, and SQL.
- Interest in a range of machine learning methodologies, with a focus on areas like natural language processing or computer vision.
- Eagerness to learn best practices in software development.
- Degree in a STEM or closely related field.
- Experience in data science, advanced analytics or a genuine interest in learning.
- Experience in data science in finance, insurance or e-commerce is an advantage but not required.
- Ability to conduct high-quality research in a suitably timely manner working both independently and in small teams as required by the task.
- Familiarity with version control and other IT delivery tools is required
- SPG Resourcing is an equal opportunities employer and is committed to fostering an inclusive workplace which values and benefits from the diversity of the workforce we hire. We offer reasonable accommodation at every stage of the application and interview process
Data Scientist, Promotion Analysis - United Kingdom - SPG Resourcing
Description
The company is described as an established and expanding player in the worldwide finance industry based in West Yorkshire.As a Data Scientist, you will play a crucial role in driving strategic decision-making through insightful analysis and innovative problem-solving.
You will have the opportunity to work across various business functions, tackling complex challenges, and contributing to the enhancement of our data-driven culture.
Developing end-to-end data solutions, from understanding intricate business issues to implementing advanced analytical models using large and diverse datasets.