Job title: Graduate Machine Learning Engineer

Locations: Manchester or Haywards Heath (hybrid working)

Role overview

Markerstudy Group has an exciting opportunity for a graduate machine learning engineer to fill out the automation, pipelining, DevOps, and modelling aspects of Markerstudy’s market-leading technical modelling and pricing team. You will productionise novel insurance modelling processes as an automated machine learning pipeline within a cloud-based environment.

Markerstudy is a leading provider of private insurance in the UK, insuring around 5% of the private cars on the UK roads, 20% of commercial vehicles and over 30% of motorcycles in total premium levels of circa £1b. Most of Markerstudy’s business is written as the insurance pricing provider behind household names such as Tesco, Sainsbury’s, O2, Halifax, AA, Saga and Lloyds Bank to list a few.

As a graduate machine learning engineer, you will work with and under the expert supervision of machine learning engineers to help build and maintain the pricing team’s MLOps and ML Lifecycle environment and support the creation of pipelines by automating the sophisticated machine learning models and processes that underpin our market-leading technical modelling and pricing function.

Key Responsibilities:

Build an MLOps / DevOps environment to support machine learning automation.
Build the pipelines that automate the regular model update and monitoring processes.
Work with the Technical Underwriting machine learning engineers to develop, implement, and maintain scalable and processes and codebase that facilitate efficient business as usual in the Risk and Retail Pricing and Underwriting Teams.
Work with the Technical Underwriting data scientists and analysts to implement and optimize new models and frameworks for production using traditional and bespoke MLOps toolkits.

Key Skills and Experience:

Experience in data science, machine learning, or insurance risk or retail pricing.
Experience in Azure ML or Databricks, or similar industry approved technology stack (i.e. AWS, Kubernetes and Docker, Google Cloud)
Understanding of machine learning models and the modelling process, from data ingestion and cleaning to deployment and modelling – from the ground-up, not only through the use of packages and libraries
Advanced technical proficiency in Python and R and good understanding of best practices and coding standards for software development (i.e. SOLID principles, Agile methodology, etc.)
Proficient at communicating results in a concise manner both verbally and written

Behaviours:

Collaborative and team player
Logical thinker with a professional and positive attitude
Passion to innovate and improve processes