This Job Vacancy has Expired!

Machine Learning Engineer

Posted on May 26, 2023 by Gerrard White

Manchester, Lancashire, United Kingdom
Immediate Start
£75k - £75k Annual

We currently have the opportunnity to work with one of the leading providers of private insurance in the whole of the UK, with total premium levels of circa £1billion!

It's a great time to join this business as they are one of the fastest growing insurers in the UK, and this is an amazing opportunity to truly progress your career.

They are on the hunt for a senior machin elearning engineer, and as a Senior Machine Learning Engineer, you will use your skills to:

Tune machine learning methods to best leverage our state-of-the-art processing capabilities

Deploy and maintain machine learning methods in a DevOps/MLOps based machine learning environment

Create robust high-quality code using test-driven development (TDD) techniques and adhering to the SOLID coding standards

Your work will enable sustained improvements to products, prices and processes giving Markerstudy a critical advantage in the increasingly competitive insurance market by minimizing the development to deployment and monitoring stages of the ML life cycle through automation.

You will also be responsible for refining, tuning, deploy and maintain machine learning methods in our machine learning pipeline by using robust test-driven development (TDD) approaches to maximise performance and robustness, and improve company performance and our customer-centric offerings across Motor, Home and Commercial Lines businesses. The successful candidate will also enjoy opportunities for building, leading, coaching, and mentoring their own specialist team of ML Engineers.

Key Responsibilities:

Report and communicate with the Senior Machine Learning Stakeholders, such as the Head of Machine Learning, Assistant Head of Machine Learning, and Head of Technical Underwriting

Propose, proof-of-concept, develop, and proof-of-delivery novel machine learning processes that automate current manual processes, and leverage DevOps and MLOps software.

Lead and work in a collaborative with data science, machine learning, IT environment to help deploy machine learning methods that are state-of-the-art, robust, and future extensible.

Tune machine learning methods for optimal performance.

Deploy and maintain machine learning methods in our machine learning pipeline using robust test-driven development (TDD) coding approaches, using the SOLID software development principles.

Actively contribute to creating a culture of coding and data excellence

Create solutions across a range of markets, including Private Motor, Commercial Vehicle, Bike, Taxi, and Home

Lead, mentor, and coach a small, specialized team of junior machine learning engineers

Key Skills and Experience:

Previous experience in tuning and deploying machine learning methods

Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering

Experience in DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks, such as AWS, Databricks, Google Cloud, etc.

Experience in creating production grade coding and SOLID programming principles, including test-driven development (TDD) approaches

Experience in programming languages (eg Python, PySpark, R, SAS, SQL)

Experience in source-control software, eg, GitHub

Proficient at communicating results in a concise manner both verbally and written


A high level of professional/academic excellence, educated to at least a master's level in a STEM-based or DS/ML/AI/or mathematical discipline

Collaborative and team player

Logical thinker with a professional and positive attitude

Passion to innovate and improve processes

This role requires travel into Manchester at least once or twice a week, so this is a flexible hybrid role.

If you feel you could be the right fit, reach out and apply now.

Reference: 2563767957


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Machine Learning Engineer in Manchester, Lancashire, United Kingdom, Full-Time

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