At a Glance
- Tasks: Lead and scale our Machine Learning Engineering capability while mentoring a talented team.
- Company: Join Hiscox, a forward-thinking company shaping the future of machine learning.
- Benefits: Autonomy in your role, competitive salary, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and continuous learning.
- Why this job: Make a real impact in ML engineering and influence strategic decisions.
- Qualifications: Experience in ML systems, strong Python skills, and leadership capabilities.
The predicted salary is between 80000 - 100000 £ 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.
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.
- 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 and advocate for platforms, tooling, and scalable ML practices.
Qualifications
- Bachelor’s or 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.
Why Join Us?
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.
Lead ML Engineer employer: Hiscox Underwriting Group Services Ltd (HUGS)
At Hiscox, we pride ourselves on being an exceptional employer that fosters a collaborative and innovative work culture. As a Lead Machine Learning Engineer, you will not only have the opportunity to shape our ML capabilities but also benefit from a strong focus on employee growth through mentorship and strategic development. Our commitment to a supportive environment ensures that you can thrive while making a meaningful impact in the insurance sector.
Contact Details:
Hiscox Underwriting Group Services Ltd (HUGS) Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Lead ML Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your ML projects, especially those that highlight your leadership and technical expertise. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on both technical and soft skills. Practice explaining complex concepts in simple terms, as well as demonstrating your ability to lead and mentor others. We want to see how you can fit into our team!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our awesome team at Hiscox.
We think you need these skills to ace Lead ML Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Lead ML Engineer role. Highlight your experience with MLOps, production ML systems, and any leadership roles you've held. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about machine learning and how you can contribute to our team. Be sure to mention specific projects or experiences that relate to the job description.
Showcase Your Technical Skills:Don’t forget to highlight your technical expertise in Python, cloud platforms, and CI/CD pipelines. We love seeing practical examples of how you've applied these skills in real-world scenarios, so include those details!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you'll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative!
How to prepare for a job interview at Hiscox Underwriting Group Services Ltd (HUGS)
✨Know Your ML Fundamentals
Brush up on your core machine learning concepts, especially supervised and unsupervised learning, feature engineering, and model evaluation. Be ready to discuss how you've applied these in real-world scenarios, as this will show your depth of understanding and practical experience.
✨Showcase Your Leadership Skills
As a Lead ML Engineer, you'll be managing a team. Prepare examples of how you've successfully led teams or mentored others in the past. Highlight your approach to fostering collaboration and psychological safety within your team, as this aligns with the company’s values.
✨Demonstrate Technical Ownership
Be prepared to discuss your experience with MLOps platforms and CI/CD pipelines. Share specific instances where you’ve ensured the reliability and scalability of ML solutions in production. This will demonstrate your capability to own and evolve technical frameworks.
✨Engage with Strategic Thinking
Think about how you can align your sub-chapter goals with broader organisational objectives. Be ready to discuss your vision for shaping technical direction and driving upskilling within your team. This shows that you’re not just a technical expert but also a strategic thinker.
