Data Scientist, Aviva

• MSc in Computer Science, Engineering, Mathematics, Statistics, Physics, or a related field (PhD preferred)
• 2+ years of experience across the end‑to‑end model development lifecycle, working with large and complex datasets (industry or academic/post‑doctoral experience considered)
• 2+ years of experience programming in Python, with a solid understanding of software engineering best practices (modularity, code reusability, version control, repositories)
• Proficiency in SQL and Git, with hands‑on experience collaborating in shared codebases
• Experience productionizing machine learning models, including monitoring, maintenance, and MLOps practices
• Familiarity with data warehouse concepts, ETL strategies and data engineering best practices
• A strong track record of building robust, maintainable, high‑quality code
• Ability to operate optimally in a data‑driven software engineering environment and translate data into business value
• Strong communication and collaboration skills, with the ability to work across disciplines

Desirable

• Join an exciting team of data scientists and engineers at the forefront of using data to drive decisions at every level of our organization. The insurance industry is undergoing a transformation and you get to be in the driver’s seat of this data-driven, technology revolution
• As a Data Scientist on the Fraud Data Science team, you’ll work closely with a wide range of business partners and help shape future products and solutions
• You’ll contribute to high‑impact initiatives focused on fraud detection, using machine‑learning‑based solutions to protect customers and the business
• You’ll be involved across the full development lifecycle, from idea generation and experimentation through deployment, monitoring, and ongoing support. Your work will include building and deploying data pipelines, machine learning, and statistical models used in real‑world applications at scale
• The team’s models are already running in production, and you’ll help expand and evolve these capabilities as part of our ongoing InsurTech transformation
• Design, develop, test, and deploy scalable, production‑ready code and machine learning models
• Transform large, complex datasets into actionable insights, recommendations, and data‑driven decisions
• Develop innovative approaches to pattern recognition using machine learning, statistical, and analytical techniques
• Design and deploy models to real‑time APIs, ensuring reliability, performance, and scalability
• Communicate insights and model outcomes clearly to both technical and non‑technical audiences
• Maintain, enhance, and optimize existing codebases and data pipelines
• Drive delivery accountability for both project‑based initiatives and BAU work, aligned to agreed timelines and priorities
• Ensure solutions conform to IT and Enterprise Architecture standards where applicable