Data Scientist

AEGIS London

. Contribute across the full model lifecycle, from data preparation and development through deployment and monitoring .

Posted 11/10/2026full-timeLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Python for data science, machine learning, and data wrangling, with a strong focus on deploying models and ensuring their operational health. Capable of communicating complex technical concepts to non-technical stakeholders and linking analytical work to business outcomes.

Highest-signal resume keywords

Python For Data ScienceMachine Learning KnowledgeSQL AbilityData WranglingClear Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical FoundationsData PreparationModel DevelopmentVersion ControlData AnalysisHypothesis TestingRegressionAutomated TestingData Visualization

Soft Skills

Clear CommunicationCuriosityCollaboration

Tools & Technologies

Azure Machine LearningGitPower BIDockerPySpark

Industry Keywords

Insurance ExperienceLloyd's MarketActuarial MethodsBusiness OutcomesMLOps

Tech Stack

Tools & technologies

AzureDockerNumpyPandasPySparkPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Contribute across the full model lifecycle, from data preparation and development through deployment and monitoring
Deliver data science projects from data preparation and exploratory analysis through model development and validation
Work with the team to deliver models to production
Take increasing ownership of deployment and in-life monitoring of built models
Apply data science techniques to automate manual processes
Explore generative AI use cases and assess their value and roadmap fitness
Assess the quality and usefulness of internal and external data sources with data scientists and actuaries
Produce ad hoc analytics and business insights coordinated through the Data Science and Data Analytics Manager
Write clean, tested, version-controlled Python and participate in code reviews
Contribute to Azure machine learning pipelines and learn MLOps and CI/CD patterns
Monitor deployed products and engage with the business to track and measure adoption
Document models clearly and explain them appropriately for a regulated environment
Work with underwriting teams to understand operations and identify where data science can add value
Capture and document requirements for data science products
Work with IT and Data Engineering on project data
Communicate findings and limitations clearly to non-technical audiences

Requirements

What you’ll need

Solid Python for data science, including readable, well-structured code and understanding of functions, classes and modules
Machine learning knowledge across scikit-learn, pandas, NumPy, statsmodels or equivalents
Sound statistical foundations, including hypothesis testing, regression, quantifying uncertainty and recognising unsupported conclusions
SQL ability to query and join relational data efficiently
Data wrangling and preprocessing of 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
Genuine interest in how models enter operational use and remain healthy in production
Curiosity about the business and willingness to learn its component parts
Desirable: evidence of linking analytical work to business outcomes
Desirable: experience working directly with business stakeholders on requirements or adoption
Desirable: insurance experience, ideally in the Lloyd's market, pricing or underwriting, or exposure to actuarial methods
Desirable: object-oriented design, automated testing such as pytest and code review
Desirable: experience taking a model into production or contributing to one
Desirable: exposure to MLOps and CI/CD concepts
Desirable: dependency and environment management such as virtual environments and Docker
Desirable: distributed processing frameworks such as PySpark
Desirable: data visualisation and reporting such as Power BI

Benefits

Comp & perks

Hybrid working pattern
Flexible working practices
Support and reasonable adjustments to ensure an accessible application process
Employee development and individual ownership for growth and success
Pairing, code review and hands-on project work to develop software engineering, cloud ML and MLOps skills