Data Scientist

Overview

As a Data Scientist at Hiscox, you will solve high-impact business problems with advanced analytics and ML, partnering across functions to drive evidence-based decisions in a fast-paced environment. You will contribute to a growing data culture and communicate the business value of analytical work to stakeholders. This role is part of an award-winning team known for innovative collaborations, including an AI-enhanced lead underwriting solution with Google. You’ll shape end-to-end data solutions and apply cutting-edge techniques to real-world insurance challenges.

Responsibilities

• Develop inputs for business information using industry standards and research to drive innovative approaches
• Design and implement end-to-end data solutions with diverse data sources, including third-party data, using ML and Generative AI techniques
• Collaborate with data scientists and product/business teams to deliver analytics
• Contribute to the data and analytics community at Hiscox to create value through analytics

Key requirements

• Degree in STEM or closely related field (equivalent experience); further degree is a plus
• Practical data science experience solving business problems and delivering insights
• Experience with data analysis, experimentation, and model development; familiarity with generative AI tools for research and development
• Ability to review and own AI-assisted work for accuracy, explainability, and fitness for purpose; works well independently and in teams
• Experience with statistical analysis, ML, and data science techniques to extract insights and support decisions
• Proficiency in Python and SQL; working knowledge of LLMs, prompt engineering, AI-assisted coding tools, and AI workflows
• Willingness to learn and apply engineering best practices (version control, testing, code review, reproducible workflows)
• Exposure to Google Cloud Platform is advantageous; knowledge of insurance industry is beneficial
• Strong verbal, written, and presentation skills for technical and non-technical audiences
• Cross-functional collaboration and teamwork
• Adaptability and eagerness to learn about new technologies
• Statistical analysis and machine learning techniques
• Python and SQL programming
• Generative AI techniques and LLMs

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