At GSK we see a world in which advanced applications of Machine Learning AI will allow us to develop transformational medicines using the power of genetics, functional genomics machine learning. AI will also play a role in how we diagnose use medicines to enable everyone to do more feel better live longer. It is an ambitious vision that will require the development of products at the cutting edge of Machine Learning AI.
The opportunities fmachine learning extend to many other areas of our business, including medicine safety, manufacturing, supply chain. To realize these opportunities, GSK has created a global Artificial Intelligence Machine learning group (AI/ML), with locations in London, San Francisco, Boston, Philadelphia, Tel Aviv, Heidelberg, to focus on the development application of machine learning to problems of critical importance at GSK. We are now embarking on an AI/ML team in China, to bring cutting-edge solutions to real-world challenges in the healthcare domain. We possess a worldwide data computational environment (including specialist hardware) to enable large-scale, scientific experiments that exploit GSK’s unique access to data.
By actively engaging with the machine learning community publishing our research, code models built on public data, the AI/ML group operates at the cutting-edge of machine learning research. To help us, we seek a passionate Machine Learning Engineer who wishes to turn their talents to the healthcare sector. You will be working with other engineers on building products to support multiple large-scale projects within AI/ML. In addition, the engineer will learn about the pharmaceutical industry software engineering translate their research tools that aid discovery development of transformational medicines.
You will have access to outstanding experts in biology, chemistry, (software) engineering, data science machine learning; unrivalled data sources GSK’s state-of-the-art laboratory compute infrastructure to help you develop validate your machine learning research.
As a Machine Learning Engineer focusing on clinical trials respiratory diseases you will:
· Design, implement, own AI/ML-driven solutions across the model development life cycle.
· Research develop state-of-the-art machine learning models, including deep learning, fbiomedical prediction tasks.
· Deliver robust, tested, performant code in an agile environment.
· Collaborate with experts in biology, medicine, experimentation to optimize data collection ftraining biomedical ML models
· Develop embed automated processes fpredictive model validation, deployment, implementation.
· Develop embed automated processes fpredictive model validation, deployment, implementation.
· Deploy your algorithms to production to identify actionable insights from large databases.
Why you?
Basic Qualifications:
We are looking fprofessionals with these required skills to achieve our goals:
A minimum of a master’s degree in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, related field
Experience in Python programming knowledge in machine learning statistics
Demonstrated proficiency working with biological/healthcare data in an academic professional setting
Familiarity with at least one Deep Learning framework such as TensorFlow, Keras PyTorch
Proven ability to solve complex problems using creative approaches, state-of-the-art tools, best engineering practices
Ability to work both autonomously collaboratively on complex projects
Preferred Qualifications:
If you have the following characteristics, it would be a plus:
A PhD in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, related field
Academic industry experience in the biomedical sciences especially in respiratory diseases
Priexperience working in clinical trials
Experience with real world evidence clinical trial data
Expertise developing machine learning models within the PyTorch framework
Experience with theory & applications in software engineering; training operating algorithms at scale; production deployment of ML services
Ability to digest, synthesize, implement innovative methods from scientific literature
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