Senior Machine Learning Engineer

Job title: Senior Machine Learning Engineer

Locations: Manchester or Haywards Heath (hybrid working)

Role overview

Markerstudy Group are looking for a Senior Machine Learning Engineer to help take leading-edge and novel insurance risk modelling and pricing techniques and participate in creating fully automated machine learning pipelines.

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 £1 billion. 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 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 lifecycle through automation.

You will also be responsible for refining, tuning, deploying and maintaining 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 leading, coaching, and mentoring more junior ML Engineers.

Key Responsibilities:

• Report and communicate with Senior Stakeholders, such as the Head of Data Science and Machine Learning and Director of Technical Underwriting
• Propose, proof-of-concept, develop, and deliver novel machine learning processes that automate current manual processes, and leverage DevOps and MLOps software.
• Work in a collaborative environment with data science 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
• Implement efficient solutions across a range of markets, including Private Motor, Commercial Vehicle, Bike, Taxi, and Home
• Lead and mentor junior machine learning engineers and share best practices

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 with deploying services in Docker and Kubernetes
• Experience in creating production grade coding and SOLID programming principles, including test-driven development (TDD) approaches
• Experience in programming languages (e.g. Python, PySpark, R, SAS, SQL)
• Experience in source-control software, e.g., GitHub
• Proficient at communicating results in a concise manner both verbally and written
• Experience in data and model monitoring is a plus

Behaviours:

• 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

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