Lead ML Engineer

As a Lead Machine Learning Engineer (MLE) at Hiscox, you will shape and scale our Machine Learning Engineering capability and ensure the successful deployment and operation of ML in production. You will lead the MLE sub-chapter, line manage Machine Learning Engineers, and partner closely with the Head of Data Science, the Data Science sub-chapters and Platform/Group teams to enable scalable, reusable, and well-governed ML solutions.

You will be accountable for the MLOps platform, ensuring it is reliable, secure, and continuously evolved and for ensuring our business unit ships ML to production in a scalable way that is reusable across value streams, enabling efficient model maintenance, monitoring, and lifecycle management. Combining deep technical expertise with leadership, you will set standards, uplift capability, and enable squads to deliver robust, production-grade ML systems.

• People Leadership
  • Manage and grow talent: Set objectives, conduct performance reviews, and guide career progression for the MLE sub‑chapter.
  • Foster a strong engineering culture: Promote collaboration, psychological safety, and high standards of quality and reliability.
  • Provide coaching and mentorship: Support technical and professional development of Machine Learning Engineers.
• Strategic Capability Development
  • Define and evolve chapter strategy: Align sub-chapter goals with chapter and organisational objectives.
  • Shape technical direction: Establish standards for ML engineering, deployment patterns, and MLOps.
  • Drive upskilling and cross‑skilling: Build capability in production ML, platform usage, and software engineering best practices.
• Technical Enablement & Platform Ownership
  • Own and evolve the MLOps platform: Ensure it is reliable, secure, and scalable, in partnership with Group and Platform teams.
  • Enable scalable and reusable ML delivery: Ensure ML solutions for the business unit are deployable across value streams and efficient to operate.
  • Lead technical spikes and proof‑of‑concepts: De‑risk architectural decisions and explore new tools and approaches.
• Governance & Standards
  • Ensure compliance, security, architecture, and operational standards.
  • Define guardrails for production ML systems: Covering deployment, monitoring, retraining, and decommissioning in collaboration with Data Science.
• Collaboration & Influence
  • Partner closely with the Data Science sub-chapters and delivery team to ensure effective handover from experimentation to production.
  • Represent Machine Learning Engineering in strategic forums: Advocate for platforms, tooling, and scalable ML practices.

• Bachelor’s/Master’s in Computer Science, Engineering, or a related quantitative field (or equivalent experience).
• Experience as a Senior/Lead Machine Learning Engineer delivering production ML systems at scale.
• Solid understanding of core data science concepts, including supervised and unsupervised learning, feature engineering, and model evaluation.
• Working knowledge of statistical concepts and model evaluation techniques sufficient to review, validate, and productionise data science work.
• Proven line management and/or technical mentorship of engineers; building capability and setting standards.
• Demonstrated ownership of MLOps platforms or critical ML services, including CI/CD, model serving, monitoring, and incident management.
• Proven ability to design, implement, and operate technical frameworks for evaluating the commercial impact of machine learning systems in production.
• Effective collaboration with Data Scientists across the end-to-end ML lifecycle.
• Experience working in Agile, cross-functional squads.
• Insurance or financial services experience is a plus but not essential.

• Strong Python in a machine learning engineering context, with solid software engineering fundamentals (OOP, testing, design patterns).
• Production ML systems: Experience deploying, monitoring, and maintaining ML models in live environments.
• Cloud & infrastructure: Hands-on experience with a major cloud platform (GCP, AWS, or Azure), including containerised deployments.
• MLOps & CI/CD: Experience with CI/CD pipelines, Git-based workflows, and Infrastructure as Code (e.g. Terraform).
• Operational excellence: Understanding of API operations, monitoring, logging, and reliability considerations for ML services.
• Data & integration: Working knowledge of SQL and integrating ML services into wider data and application ecosystems.

This is an opportunity to shape the future of machine learning engineering at Hiscox, build a high-performing sub-chapter, and influence strategic decisions, while staying close to the craft you love. You’ll have the autonomy to set standards, mentor talent, and explore emerging technologies, all within a collaborative and forward-thinking environment.

If you want to help build a brilliant future; work with amazing people; be part of a unique company culture; and, of course, enjoy great employee benefits that take care of your mental and physical wellbeing, come and join us.