Job description Posted 02 August 2021

Senior ML Engineer - deliver ML into production

£700 per day via Umbrella

6 months initially

Brentford / remote

Our organisation is undergoing agile transformation. This role is ideal for a person who can provide leadership and vision to transform the organisation into a product and value-led mindset. You will be confident in working in partnership with senior stakeholders from across the business. You will use your extensive scientific background to discover business value across our extensive data landscape. 

This is a unique opportunity which will work alongside a team of highly skilled Data Scientists & Engineers on the transformation of engineering to support AI initiatives. You will be supporting the exploration of cutting edge ML techniques in the marketing and digital-marketing space to discover patterns and predictions to help solve customer needs. You will help lead complex and diverse projects to leverage Data Science techniques which will result in measurable commercial benefit. 

You will also have the opportunity to help build our wider data science community, as we invest in academic partnerships, mentor and apprenticeship schemes. You will be seen as a leader in the machine learning engineering discipline and your thought leadership will command attention in the wider data science community. GSK has long supported the development of its employees and you will be supported to grow and progress in your career.

Functional Requirements

  • Contribute to all aspects of the Data Science Project Lifecycle, with a focus on scalable operation and production.
  • Can coordinate and manage competing priorities across a portfolio of projects
  • Provide leadership and guidance on the development and monitoring of ongoing data engineering pipelines and the delivery of ML models from prototypes to production.
  • Own and define the key performance indicators (KPIs) and diagnostics to measure performance against business goals
  • Ability to influence across organisations, proven collaboration skills, comfortable working with ambiguity, ability to make quick, informed decisions taking into account trade-offs.
  • Can conceptualize, formulate, prototype and implement ML pipelines to solve business problems
  • Proven extensive experience developing and implementing Machine Learning engineering pipelines on large data sets


Domain Expertise

  • Have a good working knowledge cloud based and local data science frameworks and toolkits.
  • Are experienced in Agile methodologies and the hypothesis-driven approach
  • Have a deep knowledge of a sufficiently broad area of technical specialism in ML engineering methodology and best practices and are a valued and trusted expert
  • Have a practical experience productionising machine learning, Deep Learning and natural language understanding/processing models
  • Experience with Azure beneficial, but not a requirement