Machine Learning Technical Lead Full Time

LNER
We do things differently here at LNER – we take a business-challenge led approach, which means as our Machine Learning Technical Lead you will be working with a whole host of business areas to build machine learning models to meet their needs, alleviate pain points or generate revenue. You certainly won’t get bored of the opportunities that come your way.

The railway is a visible service used by millions, meaning your work will be impactful and have wide reaching implications. You will work within a team of product managers, data scientists and machine learning engineers to deliver projects from start to finish – from ideation, to proof of concept, production, and maintenance/business as usual.

What will the Machine Learning Technical Lead be involved in?

The Technical Lead forms an essential part of LNER’s machine learning team, guiding and enabling our journey to maximise benefits available from implementing predictive analytics, machine learning and artificial intelligence (AI) across LNER to deliver measurable business results.

In this role, you’d be responsible for leading, supporting and mentoring a team of scientists and Engineers to re-engineer data to be ‘machine learning ready’ and then develop statistical algorithms to implement solutions to key business challenges.

You’ll also be involved in .

Preparing data for 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, in order 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.
You’ll also need to thrive working in a fast-paced environment and be comfortable with a ‘test and learn’ approach and not be afraid to experiment with creative solutions.

Does this sound like the ideal opportunity for you?

Of course, with this being such a specialised role, there are some ‘must haves’ in terms of experience.

You’ll not only need to have strong experience as a Data Scientist, but you’ll also need to have successfully led and developed other Data Scientists or Engineers.

You’ll also need to be

Fluent in writing well-structured Python code for machine learning algorithms,
Experienced in taking on novel problems, developing algorithms and implementing productionised operational solutions to drive the benefit of machine learning
A solid grasp and demonstratable knowledge of standard data science techniques, for example, supervised/unsupervised machine learning, model cross validation, Bayesian inference
Confident testing code and troubleshooting – curious to explore and find the outcome, fast, to ensure that errors are dealt with in a timely manner
A good understanding of coding best practices and experience with code and data versioning (using Git/CodeCommit), code quality and optimisation, error handling, logging, monitoring, validation and alerting.
An excellent command of the basic libraries for data science (g. NumPy, Pandas)
Experience in working with AWS Machine Learning Stack and visualisation tools would be advantageous
Be an effective communicator, able to translate technical information to a non-technical audience

Ultimately, we’re looking for passionate people who will thrive on the challenge of demonstrating the value we can add as a Machine Learning team.

So, if you’re ready for a new challenge and want to put your stamp on these new roles, what are you waiting for? Apply now!