AI/ML Engineer, Causality
- Cambridge, MA
Site Name: USA - Massachusetts - Cambridge
Posted Date: Jun 4 2020
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:
- Create algorithms to discover causal relationships within high-dimensional genomic datasets
- 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
- Deploy your algorithms to production to identify actionable insights from large databases
- Develop and embed automated processes for predictive model validation, deployment, and implementation
Why you? Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:
- A higher degree in computer science, applied math, physics, systems biology, bioinformatics, or related field
- Experience using a programming language such as Python.
If you have the following characteristics, it would be a plus:
- 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
- Excellent written and verbal communication skills
- Ability to work autonomously and collaboratively as part of a team to both teach and learn every day
Our values and expectationsare 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, rigour 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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GSK is an Equal Opportunity Employer and, in the US, we adhere to Affirmative Action principles. This ensures that all qualified applicants will receive equal consideration for employment without regard to race, color, national origin, religion, sex, pregnancy, marital status, sexual orientation, gender identity/expression, age, disability, genetic information, military service, covered/protected veteran status or any other federal, state or local protected class.
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