At a Glance
- Tasks: Lead and scale our Machine Learning Engineering capability while managing a talented team.
- Company: Join Hiscox, a forward-thinking company at the forefront of ML innovation.
- Benefits: Competitive salary, career growth, and a supportive work environment.
- Other info: Dynamic role with opportunities for mentorship and collaboration across teams.
- Why this job: Shape the future of ML solutions and make a real impact in production.
- Qualifications: Experience in ML engineering and strong leadership skills required.
The predicted salary is between 60000 - 80000 £ per year.
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.
Key Responsibilities
- 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.
Qualifications
- 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.
Technical Skills
- 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.
Lead ML Engineer in London employer: Hiscox Inc.
At Hiscox, we pride ourselves on being an exceptional employer, particularly for our Lead Machine Learning Engineer role. Our collaborative work culture fosters innovation and psychological safety, while our commitment to employee growth ensures that you will have ample opportunities for professional development and mentorship. Located in a dynamic environment, we offer a robust MLOps platform and the chance to lead a talented team, making a meaningful impact in the insurance sector through scalable and reusable ML solutions.
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML Engineer in London
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Hiscox Inc.!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Lead ML Engineer at Hiscox Inc..
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Hiscox Inc..
✨Apply Directly through Our Website
When you find a suitable opening like Lead ML Engineer at Hiscox Inc., make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Lead ML Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Hiscox Inc., your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Hiscox Inc.. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Hiscox Inc.
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Hiscox Inc.!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
