Machine Learning Engineer – Lner Job Vacancies Near Me In Manchester

Overview

Position Title: Machine Learning Engineer

Company: London North Eastern Railway

Location: Manchester, UK

Job Description

The Machine Learning Data Engineer forms an essential part of LNER’s Data Science Team, primarily by manipulating, cleansing and transforming data to be ‘machine learning ready’. This is a pivotal role in the team by partnering with key technical stakeholders, such as Solutions Architects and the IT Department, to support the build of productionised solutions, to enable the benefits of data science solutions to be realised.

Responsibilities:

• Working closely with data scientists by preparing data for them to develop statistical algorithms to implement solutions to key business challenges and advising on the best approach to do this.
• Mapping out data feeds and systems in collaboration with Solutions Architects and the IT Team, to then build richer pools of data for proof of concept/production solution design and build.
• Develop best practices for ML Ops, engineering tasks, code development, code deployment, ethics, and approach to productionising solutions.
• Leading on data engineering and ETL tasks which will exploit the AWS machine learning stack to examine novel machine learning problems within the rail industry.
• Provide quality assurance of engineering tasks by code checking and any other practices necessary
• Conducting feasibility and practicality testing of business challenge-led machine learning ideas, to help strengthen the data science portfolio.

Requirements:

• A proven track record of creating and designing data pipelines, exposing and linking data from multiple systems
• Experience with security and monitoring best practice, preferably using AWS Cloud infrastructure
• Technical experience of mapping out data feeds to integrate and separate data to produce, transform and test new machine learning ideas
• Fluent in writing well tested, readable code using Python that is capable of processing large volumes of data.
• An excellent command of the basic libraries for data science (e.g. NumPy, Pandas)
• Experience of iteratively making data ‘machine learning ready’ preferably within AWS machine learning stack (primarily SageMaker)