Machine Learning Engineer

Job title: Machine Learning Engineer Locations: Flexible Role overview Markerstudy Group have an exciting opportunity for a 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 our Machine Learning Engineer, you will help build and maintain the pricing team’s MLOps and ML Lifecycle environment to 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 Build a framework that supports the creation, deployment, maintenance, and monitoring elements that non-data scientist and machine learning analysts produce, including assisting with hyper-parameter tuning, feature engineering, feature selection, and validation, reporting and visualisation, and communication processes. Work closely with the data engineering team to integrate directly with regular data feeds Key Skills and Experience: Previous experience as a DevOps / MLOps engineer 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 Proficient at communicating results in a concise manner both verbally and written Previous industry experience in a STEM role or educated to the Master’s level in a STEM or DS / ML / AI or maths-based discipline. Behaviours: Collaborative and team player Logical thinker with a professional and positive attitude Passion to innovate and improve processes Strong grasp of industry standards, and proficient in either Python, R, or both