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- Tasks: Own the deployment framework for data science services and automate the data science lifecycle.
- Company: Join Hiscox, an innovative international specialist insurer focused on diversity and inclusion.
- Benefits: Enjoy competitive salary, 25 days leave, gym subsidy, and a four-week sabbatical every five years.
- Why this job: Be part of a collaborative team, working on cutting-edge data science projects that drive real business impact.
- Qualifications: Strong Python skills and experience with cloud-native deployments; knowledge of machine learning is essential.
- Other info: Work in a hybrid environment with a focus on innovation and professional growth.
The predicted salary is between 43200 - 72000 £ per year.
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Responsibilities
- Ownership of the deployment framework for all data science services, overseeing data flow into the data science lifecycle from the business data warehouse.
- Oversight of automation of the data science lifecycle (dataset build, training, evaluation, deployment, monitoring) during production.
- Good understanding of core data science principles and challenges in migrating research code into production.
- Ability to work closely with a team and collaborate on all aspects of data science and deployment lifecycle.
- Collaborate with data scientists, data engineers, and other technical teams, including pricing teams, to support analytics maturity within the organization.
- Write high-quality Python code following industry best practices for model training and deployment.
Required skills:
- Strong Python programming skills.
- Knowledge of software engineering best practices.
- Experience with TDD (pytest or equivalent).
- Experience with cloud-native deployments (Databricks and Azure).
- Experience with Databricks, managed endpoints, AKS or equivalent.
- Experience with Version Control Systems (VCS) and CI/CD pipelines.
- Understanding of applying machine learning to solve business problems.
- Experience in developing predictive and prescriptive analytics (machine learning, data mining) to derive business insights and communicate findings effectively.
Desirable Skills:
- Graduate or postgraduate qualification or equivalent experience in engineering, mathematics, physics, or statistics.
- Experience in finance, insurance, or e-commerce data science (preferred but not required).
- Experience with neural networks, TensorFlow, CatBoost, XGBoost, SKlearn, Pandas.
Our technology:
We are developing a data platform in Databricks that encompasses all UK business unit data, supporting the data science team. The ML Engineer will leverage and extend this platform to deliver comprehensive end-to-end data science services.
Rewards:
- Competitive salary with a wide range of benefits.
- 25 days annual leave plus two Hiscox days.
- Four-week paid sabbatical after every five years of service.
- Contributory pension.
- Additional benefits: gym membership subsidy, Christmas gift, four times life insurance.
About us:
At Hiscox, we value our people, foster diversity, and strive for an inclusive culture that drives success. As an international specialist insurer, we focus on key areas of expertise, encouraging innovation and challenging convention.
#LI-EBI #LI-HYBRID
Work with amazing people and be part of a unique culture. Job ID R0017401
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Machine Learning Engineer employer: Hiscox Ltd
Contact Detail:
Hiscox Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Familiarise yourself with the specific technologies mentioned in the job description, such as Databricks and Azure. Having hands-on experience or projects showcasing your skills with these platforms can set you apart from other candidates.
✨Tip Number 2
Engage with the data science community by participating in forums or attending meetups related to machine learning and data engineering. Networking with professionals in the field can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Showcase your collaborative skills by highlighting any past experiences where you worked closely with cross-functional teams. This could include projects where you partnered with data scientists or engineers, demonstrating your ability to contribute effectively in a team environment.
✨Tip Number 4
Prepare to discuss how you've applied machine learning to solve real business problems in previous roles. Be ready to share specific examples of predictive or prescriptive analytics projects that led to actionable insights, as this aligns well with the responsibilities of the role.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong Python programming skills and experience with cloud-native deployments. Emphasise any relevant projects or roles that demonstrate your understanding of data science principles and your ability to work in a team.
Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the Machine Learning Engineer role at Hiscox. Mention specific experiences that align with the responsibilities outlined in the job description, such as your familiarity with TDD, CI/CD pipelines, and your experience in developing predictive analytics.
Showcase Relevant Projects: If you have worked on projects involving machine learning, data mining, or cloud technologies like Databricks and Azure, be sure to include these in your application. Provide links to your GitHub or portfolio if applicable, showcasing your high-quality Python code.
Highlight Soft Skills: Since collaboration is key in this role, mention any experiences where you successfully worked with cross-functional teams. Highlight your communication skills and how you've effectively conveyed complex data insights to non-technical stakeholders.
How to prepare for a job interview at Hiscox Ltd
✨Showcase Your Python Skills
Make sure to highlight your strong Python programming skills during the interview. Be prepared to discuss specific projects where you've applied Python for model training and deployment, and demonstrate your understanding of software engineering best practices.
✨Understand the Data Science Lifecycle
Familiarise yourself with the entire data science lifecycle, from dataset build to monitoring. Be ready to discuss how you have overseen automation in previous roles and how you can contribute to the deployment framework at the company.
✨Collaborate Effectively
Emphasise your ability to work closely with teams, including data scientists and engineers. Prepare examples of past collaborations that led to successful outcomes, showcasing your teamwork and communication skills.
✨Prepare for Technical Questions
Expect technical questions related to cloud-native deployments and CI/CD pipelines. Brush up on your knowledge of Databricks, Azure, and version control systems, and be ready to explain how you've used these tools in your work.
