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For Companies At a Glance
- Tasks: Shape the data science delivery pipeline and automate the data science lifecycle.
- Company: Join Hiscox, a company committed to diversity, innovation, and finding better solutions.
- Benefits: Enjoy 25 days annual leave, a four-week sabbatical, and performance-related bonuses.
- Why this job: Be part of a dynamic team influencing decisions through data science in a unique culture.
- Qualifications: Graduate or postgraduate in engineering, mathematics, or equivalent experience required.
- Other info: Work with cutting-edge technology on a new data platform in Databricks.
The predicted salary is between 28800 - 48000 £ per year.
Job Type:
Permanent
Build a brilliant future with Hiscox
About Hiscox
Hiscox UK is a leading brand in the insurance market, recognised as setting the standards others try to emulate. We consistently deliver strong growth and exceptional returns, recruiting only the very best and empowering them to deliver. We are known for insuring the homes of the rich and famous through to the most innovative technology companies. Our customers are diverse and unique and are only united by our ability to provide specialist insurance tailored to their needs.
The Team
The Hiscox UK Data Science team operates across the UK business unit, providing data-driven insights that inform strategic decision-making and operational improvements. We specialise in machine learning and generative AI solutions to address complex business challenges in collaboration with stakeholders across the business. We deliver robust, scalable models and analytical solutions that drive innovation and support evidence-based decisions.
The Role
We’re looking for a talented and pragmatic Machine Learning Engineer to join our growing data science team. We’re working on a wide range of greenfield projects, from fraud detection to generative AI, giving you the chance to help shape solutions from the ground up. You’ll be shaping the full machine learning lifecycle, collaborating closely with data scientists and engineers in a cross-functional environment to define how we solve problems with data science. This role is key to ensuring that models developed in research are successfully transitioned into scalable, production-ready solutions.
This role is suited to individuals who are passionate about data and committed to software engineering best practices, with a drive to innovate and advance organisational capabilities.
Key Responsibilities
Contribute to the design and evolution of our Data Science platform, helping define best practices, tooling and the ML Engineering function as the team and project portfolio grow.
Have a strong voice in the automation of the end-to-end data science lifecycle, leveraging CI/CD and infrastructure as code to support scalable, enterprise-grade production workflows.
Work closely as a team, collaborating on all aspects of the data science and deployment lifecycle across traditional ML and generative solutions.
Work collaboratively with dependency teams including data engineers, software engineers and business stakeholders.
Write high quality python code following industry best practice for model development, deployment and maintainability.
Contribute technically to the data science modelling and project workflows, helping select modelling approaches, participating in architecture discussions, and deployment strategies.
Candidate Profile
Skills and experience:
Proven track record in data science or ML engineering roles within a business setting
Strong python programming skills and wider software engineering best practice
Strong communication skills including translation of technical concepts for non-technical stakeholders
Good understanding of core data science principles
Experience with production-level cloud-native deployment of machine learning services, using containerisation, Kubernetes or equivalent. We work across an Azure and Databricks estate, therefore experience with these platforms would be particularly beneficial
Utilisation of an industry-standard software stack for data and software, including VCS (git), CI/CD (Azure DevOps desirable) and Project Management (JIRA)
Experience deploying data science models to solve real-world business problems in production, ideally within a regulated industry such as finance or insurance
Experience utilising LLMs, generative or agentic AI in a commercial setting is beneficial
Recruitment Process
Our hiring process is designed to be thorough yet transparent, giving you the opportunity to showcase your skills and learn more about us. Here’s what you can expect:
Initial Screening Call – An initial conversation with a member of our Talent Acquisition team to discuss your skills and experience and interest in the role.
Informal Call with the Hiring Manager – An opportunity to talk through your CV and learn more about the position.
Technical Take-home Task – A technical exercise (approx. 2–3 hours to complete) to demonstrate your ability. We’ll review this ahead of the subsequent stages and provide feedback.
Technical Interview – A deeper discussion of your technical expertise & your solution to the task.
Business Stakeholder Interview – A final conversation with key stakeholders to discuss the role’s requirements, how your skills and experience align with business objectives, and how you embody our values. This is also an opportunity for you to ask broader questions about the team, culture, and the company’s direction.
Why Join Us?
A career at Hiscox is more than just a job—it’s an opportunity to grow, thrive, and be rewarded for your contribution. Beyond a competitive salary, we offer a comprehensive benefits package designed to support your financial, physical, and personal wellbeing. From retirement plans and healthcare coverage to flexible working options and professional development support, we aim to create an environment where you can succeed both inside and outside of work.
To explore the full range of benefits available in your location, visit: Benefits | Hiscox Group
We also know that none of us ever stops learning. Whether you’re just starting out or have decades of experience, we’ll give you the tools and opportunities to nurture your talent and fulfil your potential. Our learning and development programmes include financial support for professional qualifications, world-class technical training, and a wide range of courses focused on personal growth, career progression, and leadership skills.
Diversity and Hybrid working
At Hiscox we care about our people. We hire the best people for the job and we’re committed to diversity and creating a truly inclusive culture, which we believe drives success. We operate a hybrid working model, set by the team rather than the business, to enable you to manage your own personal work-life balance. We see it as the best of both worlds; structure and sociability on one hand, and independence and flexibility on the other
Work with amazing people and be part of a unique culture
Machine Learning Engineer employer: Hiscox
Contact Detail:
Hiscox Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with Azure and Databricks, as these are key platforms for the role. Consider taking online courses or tutorials to deepen your understanding of their functionalities and how they integrate with machine learning workflows.
✨Tip Number 2
Showcase your experience with TDD and CI/CD pipelines in your discussions. Be prepared to discuss specific projects where you implemented these practices, as this will demonstrate your commitment to software engineering best practices.
✨Tip Number 3
Engage with the data science community by attending meetups or webinars focused on machine learning and cloud technologies. Networking with professionals in the field can provide insights and potentially lead to referrals.
✨Tip Number 4
Prepare to discuss how you've applied machine learning to solve real business problems. Think of specific examples where your contributions made a measurable impact, as this will resonate well with the hiring team.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, software engineering best practices, and any specific tools mentioned in the job description, such as Azure, Databricks, and Python. Use keywords from the job listing to ensure your application stands out.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for data science and your understanding of the role. Mention specific projects or experiences where you successfully deployed machine learning models or collaborated with data scientists, emphasising your ability to transition research into production.
Showcase Relevant Projects: If you have worked on projects involving predictive analytics, machine learning, or cloud-native deployments, include these in your application. Provide links to your GitHub or portfolio to demonstrate your coding skills and familiarity with tools like TensorFlow or SKlearn.
Highlight Soft Skills: In addition to technical skills, mention your ability to collaborate with teams and communicate complex ideas clearly. This is crucial for the role, as it involves working closely with data scientists and engineers to enhance analytics maturity within the organisation.
How to prepare for a job interview at Hiscox
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, Azure, and Databricks. Highlight specific projects where you've implemented machine learning models and how you overcame challenges in deploying them into production.
✨Understand the Data Science Lifecycle
Familiarise yourself with the entire data science lifecycle, from data acquisition to model deployment. Be ready to explain how you would automate processes and ensure smooth transitions from research to production.
✨Demonstrate Collaboration Skills
Since this role involves working closely with data scientists and engineers, be prepared to discuss examples of successful teamwork. Emphasise your ability to communicate complex technical concepts to non-technical stakeholders.
✨Prepare for Problem-Solving Questions
Expect questions that assess your ability to apply machine learning to real business problems. Think of scenarios where you've identified opportunities for machine learning solutions and be ready to discuss your thought process.
