8 Contract, Tech / Digital / IT jobs in City Of London

Please note, you must hold active DV Clearance and be a sole British national to be eligible for this role. Responsibilities:

• Design, develop, and test solutions to collect, integrate, and prepare data for advanced analytics and machine learning applications.
• Analyse complex datasets to uncover trends, patterns, and actionable insights that drive business or operational outcomes.
• Build, prototype, and evaluate statistical and machine learning models to solve real-world problems, testing feasibility and estimating impact before full deployment.
• Engineer and implement ML-based solutions, owning the full life cycle - from model development and deployment to monitoring and iteration.
• Deploy models into production environments, handling the integration and operationalisation of ML within wider systems and applications.
• Continuously evaluate and monitor model performance, identifying degradation, performance gaps, or opportunities for optimisation.
• Collaborate closely with data analysts, engineers, and other stakeholders to define new tools, enhance workflows, and support innovation across teams.
About You:
• You have a strong foundation in data science, analytics, or machine learning, with hands-on experience developing models that solve practical problems and deliver measurable impact.
• You are comfortable working across the full machine learning life cycle - from exploratory data analysis and model prototyping to production deployment, integration, and ongoing monitoring.
• You are proficient in Python and its data/ML ecosystem (eg pandas, scikit-learn, PyTorch, TensorFlow), and you can apply statistical and machine learning techniques confidently in real-world settings.
• You have deployed models into live systems and understand how to make ML operational - whether that means working with APIs, integrating into existing applications, or using containerisation tools like Docker
  
  You have a strong foundation in data science, analytics, or machine learning, with hands-on experience developing models that solve practical problems and deliver measurable impact.
• You are comfortable working across the full machine learning life cycle - from exploratory data analysis and model prototyping to production deployment, integration, and ongoing monitoring.
• You are proficient in Python and its data/ML ecosystem (eg pandas, scikit-learn, PyTorch, TensorFlow), and you can apply statistical and machine learning techniques confidently in real-world settings.
• You have deployed models into live systems and understand how to make ML operational - whether that means working with APIs, integrating into existing applications, or using containerisation tools like Docker