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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.
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We are looking for an experienced machine learning engineer to join a newly formed data science team.
You will have the opportunity to shape the data science delivery pipeline by building the infrastructure to acquire data from the data platform, deploy models, maintain, monitor, and upgrade core data science services in both Azure’s native platform and in Databricks. You will work closely with data scientists in a skills crossover methodology, contributing to the data science workflow, discussions, and development, while maintaining a key role in transitioning models from research into production.
The role of the Machine Learning Engineer
This role is ideal for someone passionate about using data science to influence decisions and eager to learn more about delivering value through data.
Key Responsibilities
- Ownership of the deployment framework for all data science services, overseeing data flow into the data science lifecycle from the wider business data warehouse.
- Automating the data science lifecycle (dataset build, training, evaluation, deployment, monitoring) for production environments.
- Understanding core data science principles and challenges in migrating research code into production.
- Collaborating closely with a team on all aspects of the data science and deployment lifecycle.
- Working with data scientists, data engineers, and other technical teams to support analytics maturity within the organization.
- Writing high-quality Python code following industry best practices for model training and deployment.
Required Skills
- Good knowledge of software engineering best practices.
- Experience with TDD (pytest or equivalent).
- Experience with cloud-native deployments (Azure preferred).
- Experience with Databricks, managed endpoints, AKS or similar.
- Experience with version control systems (VCS) and CI/CD pipelines.
- Ability to identify opportunities to apply machine learning to solve business problems.
- Experience in developing predictive and prescriptive analytics (modeling, machine learning, data mining).
- Graduate or postgraduate qualification in engineering, mathematics, physics, statistics, or equivalent experience.
- Experience in finance, insurance, or eCommerce is a plus but not required.
- Knowledge of neural networks, TensorFlow, CatBoost, XGBoost, SKlearn, Pandas.
Our Technology
We are developing a new data platform in Databricks that consolidates all UK business unit data. The ML Engineer will leverage and extend this platform to provide comprehensive end-to-end data science services.
Rewards
- Competitive salary and benefits.
- 25 days annual leave plus two Hiscox days.
- Four-week paid sabbatical after every five years of service.
- Performance-related bonus and contributory pension.
- Additional benefits including Christmas gift, life insurance, and more.
About us
At Hiscox, we value our people and are committed to diversity and inclusion. We focus on key areas of expertise, encouraging innovation and challenging conventions to find better solutions.
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Join us and work with amazing people in a unique culture.
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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.
