Please note that my client will only consider candidates with 2 years commercial Data Science or Machine Learning experience.
My client is a multinational financial services provider who provide over 2.5 million quotes per day - all powered by sophisticated rating and machine learning technology.
Pricing and risk analysis, core functions in finance and insurance, have always been among my clients key strengths. As these industries are rapidly evolving in the context of disruptive technologies and new business models, my client keep looking for innovative ways to stay ahead of the curve!
The International Pricing & Analytics Team are 20+ people based in Cardiff (UK), who provide strategic advice and support to all countries in the Group. They evaluate hundreds of variables from internal and external data sources to tailor the price of products for each of their customers. The Analytics function leverages new machine learning techniques to augment traditional Pricing expertise. The team travel to our overseas operations in Spain, Italy, France, and the USA to help launch new products, implement the latest technologies, and research new data sources.
About the Role
As a Machine Learning (ML) Engineer, you will be responsible for the deployment of ML models in the cloud. Your expertise will be key to advance and automate this main area of the ML lifecycle. You will have the chance to define best practices for ML deployment and test automation, as well as choose and implement the latest tools, technologies, and approaches.
This role sits between data science and IT, and you will have the opportunity to work with diverse stakeholders at different levels of the organisation such as data warehouse managers, cloud engineers, data scientists, pricing experts and senior business managers, both in the UK and abroad. Depending on project needs, you might need to travel occasionally to international businesses.
- Deploy and maintain scalable ML models in production
- Define ML lifecycle best practices and advise other divisions on them
- Research new ideas and stay up to date with industry developments
- Make recommendations on how to improve existing architectures and processes
- Communicate results to key decision-makers across geographies
Essential Skills and Experience
- Work experience deploying ML models using tools such as Domino Data Lab and Kubeflo
- Hands-on experience using native cloud services in AWS, GCP or Azure
- Experience building and deploying microservices using Docker and Kubernetes
- Ability to script/code in a language such as Python or Java
- Experience using version control systems such as git
Nice to Have
- Familiarity with feedback pipelines to re-train machine learning models
- Knowledge of Linux and comfortable using the command line
- Experience with CI/CD and automation tools such as Terraform and Jenkins
- Experience building systems to exchange information across services such as APIs
- AWS Machine Learning and/or DevOps Engineer certifications
- Extensive flexible benefits