Job Description

At GlaxoSmithKline we have created a world-leading data and computational environment to enable large scale scientific experiments that exploit GSK’s unique access to data.  Our focus is on bringing data, analytics & science together into solutions for our scientists to develop medicines for patients.

We are seeking to grow our team with brilliant and diverse contributors with technical ability. We are looking for machine learning experts that help discover new medicines by focusing on causal relationships in data.  This is an exciting role that will stretch your knowledge and curiosity, offering the opportunity to learn new skills and work within a global community.

This is a hands-on position where you will be empowered to be creative, ambitious and bold, to solve novel R&D problems and have the potential to directly impact the lives of patients living with disease. We have impressive toolkits and world class data, and we are now looking for talented people to join us.  

As a Machine Learning Engineer focusing in Causal Machine Learning we’d like you to be able to:

Design and implement novel scientific approaches to uncover causal relationships within high-dimesional -omics datasets that inform mechanism of action for drug discovery
Design, develop and implement analytical solutions using a variety of commercial and open source tools (common tools include Python, Keras or TensorFlow)
Connect and collaborate with subject matter experts in biology, genomics and medicine
Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to build, test, and validate predictive models
Develop and embed automated processes for predictive model validation, deployment, and implementation
Deploy your algorithms to production to identify actionable insights from large databases

Why you? 

Basic Qualifications: 

A graduate degree in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
Extensive experience in Python programming and knowledge in machine learning and statistics
Past experience in building deep learning models, preferably with exposure to functional genomics,  molecular and cellular biology or to modeling dynamical systems
Experience with at least one Deep Learning framework such as TensorFlow, Keras or PyTorch

Preferred Qualifications: 

A PhD in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
Understanding of  best practices in software engineering, training and operating algorithms at scale, and production deployment of ML services
Excellent written and verbal communication skills
Ability to digest, synthesize, and implement innovative methods from scientific literature
Ability to solve complex problems using creative approaches, state-of-the-art tools and best engineering practices
Ability to work autonomously and collaboratively as part of a team to both teach and learn every day

Why GSK? 

Our values and expectations are at the heart of everything we do and form an important part of our culture. These include Patient focus, Transparency, Respect, Integrity along with Courage, Accountability, Development, and Teamwork. As GSK focuses on our values and expectations and a culture of innovation, performance, and trust, the successful candidate will demonstrate the following capabilities: 

?Agile and distributed decision-making – using evidence and applying judgement to balance pace, rigors and risk 
Managing individual and team performance. 
Committed to delivering high quality results, overcoming challenges, focusing on what matters, execution. 
Implementing change initiatives and leading change. 
Sustaining energy and well-being, building resilience in teams. 
Continuously looking for opportunities to learn, build skills and share learning both internally and externally. 
Developing people and building a talent pipeline. 
Translating strategy into action - a compelling narrative, motivating others, setting objectives and delegation. 
Building strong relationships and collaboration, managing trusted stakeholder relationships internally and externally. 
Budgeting and forecasting, commercial and financial acumen. 

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