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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - **Company:** AEGIS London - **Location:** London, United Kingdom - **Salary:** £66,375 - **Contract:** Permanent contract - **Skills:** Data Analysis, Automation of Tests, Microsoft Azure, Code Review, Continuous Integration, Data Cleansing, Information Engineering, Data Transformation, Data Visualization, Relational Databases, Distributed Computing Environment, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, NumPy, Object-Oriented Software Development, Power BI, Software Engineering, SQL Databases, Data Processing, Virtual Environment, Git, Pandas, Pytest, Pyspark, Scikit Learn, Statistics Packages, Deployment Automation, Data Analytics, Machine Learning Operations, Software Version Control, Data Pipelines, Docker - **Published:** November 12, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5962890962 ## About the Role Technically, we are looking for solid Python, sound ML and statistical foundations, and good data analysis skills. Familiarity with software engineering practice, cloud ML (Azure in our case) and MLOps is a plus, and the team will invest in developing these through pairing, code review and hands-on project work., * Solid Python for data science, with readable, well-structured code and an understanding of functions, classes and modules. * Machine learning across the standard toolkit (scikit-learn, pandas, NumPy, statsmodels or equivalents), with sound judgement about validation and the limits of what the data supports. * Statistical foundations: hypothesis testing, regression, quantifying uncertainty and recognising when a conclusion is not supported. * SQL, with the ability to query and join relational data efficiently. * Data wrangling and preprocessing, including ambiguous, incomplete and inconsistent data. * Working knowledge of Git and version control. * Clear written and verbal communication, including explaining technical work to non-technical colleagues. Desirable * Evidence of linking analytical work to a business outcome, such as a cost saved, a decision changed or a process automated. * Experience working directly with business stakeholders on requirements or adoption. * Insurance experience, ideally in the Lloyd's market, pricing or underwriting, or exposure to actuarial methods. * Object-oriented design, automated testing (eg. pytest) and code review as normal practice. * Experience taking a model into production, or contributing to one that was. * Exposure to MLOps and CI/CD concepts: pipeline orchestration, model versioning, automated deployment and monitoring. * Dependency and environment management (eg. virtual environments, Docker). * Distributed processing frameworks such as PySpark. * Data visualisation and reporting, for example Power BI. ## Description Working closely with members of the Data Science and Analytics team, the holder will contribute across the full model lifecycle, from data preparation and development through to deployment and monitoring, taking on progressively more ownership of projects. Production experience is welcome but not required; what matters is a genuine interest in how models get into operational use and stay healthy once they are there. The holder will also spend time with commercial underwriters and Actuarial, learning how each class operates and how data science creates value for them. The position is suitable for someone who shows curiosity about the business and a willingness to learn it's component parts., * Deliver well-scoped data science projects, from data preparation and exploratory analysis through model development and validation. Working with team to deliver to production. * Take on increasing ownership of the full lifecycle over time, including deployment and in-life monitoring of models you have built. * Apply data science techniques to automate manual processes. * Explore generative AI use cases, helping assess their value and fitness for the roadmap. * Assess the quality and usefulness of internal and external data sources, working with data scientists and actuaries. * Produce ad hoc analytics and insights for the business, coordinated through the Data Science and Data Analytics Manager. Engineering standards * Write clean, tested, version-controlled Python that follows team standards, and take part in code review as both author and reviewer. * Contribute to machine learning pipelines in our cloud environment (Azure), learning the team's MLOps and CI/CD patterns through hands-on work. * Monitor the performance of deployed products, engaging with business to track and measure adoption. * Document models clearly and explain them to a standard appropriate for a regulated environment. Stakeholder engagement and requirements * Spend time with underwriting teams to understand how each class operates and where data science can add value. * Help capture and document requirements for data science products. * Work with IT and Data Engineering on the data that projects depend on. * Communicate findings, and their limitations, clearly to non-technical audiences., We make decisions considering the best interests of key stakeholders. We are direct and straightforward in our actions, working collaboratively to create a culture of fairness and respect. We act with integrity, valuing diversity of thought and background. We take time to listen to the needs of our customers, stakeholders and colleagues working together to seek and share information. We have a passion for success, aspiring to be recognised as best in class. We embrace new opportunities, encouraging innovation in pursuit of our goals. We strive to improve at all times, challenging complacency, being agile and adapting to change. We always seek to improve our customers' experience with us. We provide an environment where each employee can reach their personal potential. We encourage personal accountability for performance and individual ownership for growth and success.