Principal Deep Learning Engineer
hace 6 meses
Company
AstraZeneca is a global, innovation-driven biopharmaceutical business that focuses on the discovery, development and commercialization of prescription medicines for some of the world's most serious diseases. But we're more than one of the world's leading pharmaceutical companies. At AstraZeneca, we're proud to have a unique workplace culture that inspires innovation and collaboration. Here, employees are empowered to express diverse perspectives and are made to feel valued, energized and rewarded for their ideas and creativity.
Department - AI Strategy & Innovation
The AI Strategy & Innovation Team is a global team of highly experienced, skilled AI engineers and professionals, passionate about delivering state of the art services and products within R&D IT.
AI has the potential to transform every step of the R&D pipeline. Through recent advancements in technology we are now collecting trillions of points of data, and have the opportunity to harness that data for analytics and insight that will lead to a new generation of medicines that are capable of addressing the world’s most challenging unmet medical needs. The mission of our team is to transform the way the R&D uses data, analytics and AI to discover and develop medicines. In order to achieve this we partner with scientific teams to deliver cutting-edge capabilities, products and platforms that enable scientists to accelerate medicines that are safe and effective for patients.
Role
In this role, you’ll own the technical delivery and implementation of our internal deep learning capabilities.
In addition, this role will be the site leader for AI Strategy & Innovation in Barcelona. You will build a community of data experts, driving team cohesion, engagement, morale and sharing of best practice - making it both high performing and a great place to work.
Key Accountabilities
- Work closely with Product Lead for Deep Learning to build a roadmap and vision for our capability build that meets key strategic R&D needs.
- Drive clear communications, accountability across the team and effective ways of working.
- Prioritize (from technical perspective) backlog features with the Product Lead and Project Manager and key stakeholders
- Drive technical direction and architectural decisions for Deep Learning, ensuring we have the right technology to enable data scientists and AI engineers to work efficiently and accurately
- Drive the Deep Learning engineering roadmap, in line with the scientific vision
- Remain up to date with machine learning literature and modelling approaches. Actively participate in both internal and external opportunities to publish and present progress of the Deep Learning team.
- Work closely with project managers and Product Lead to optimize platform budgets and work alongside aligned projects to ensure provision of balanced and appropriate skillsets in delivery teams.
- Be a champion for high-standards to ensure best practice is developed in the Deep Learning team
- Coach, mentor and develop other members of the team, lead and inspire and be a key part of their development and retention.
- Build and support the Deep Learning team through hiring, coaching, mentoring, feedback and hands-on career development
- Design and implement technical solutions across the full stack
- Research and develop state-of-the-art machine learning models to optimize model performance on a range of biomedical prediction tasks.
- Advocate and advance modern, agile development practices and help develop and evangelize a great engineering culture
- Be able to write excellent code with proper unit, functional, and integration tests for code to ensure quality.
- Significant experience and proven deep technical skills in one or more of the following areas: machine learning, recommendation systems, pattern recognition, natural language processing or computer vision.
- Application of deep learning techniques in a life sciences context, and in particular clinical imaging.
- Experience with one or more DL frameworks such as Tensorflow or PyTorch.
- Experience with scientific and machine learning libraries e.g., SciPy, Scikit-learn, NumPy.
- Strong software development skills. Proficiency in Python preferred.
- Experience building large scale data processing pipelines.
- Experience with Cloud computing, Hadoop/Spark, SQL.
- Exposure to ML Ops principles
- Exposure to Knowledge Graph/graph technologies
- Proven experience leading a team
- Ability to explain and present analyses and machine learning concepts to a broad audience.
- Ability to work with loosely defined objectives and turning these into concrete machine learning problems.
- Experience in software engineering and machine learning best practices, including version control, continuous integration (CI) and continuous development (CD), containerization, and shell scripting.
- Background or interest in biology or medicine.
- Relevant scientific publications in AI/ML (NeurIPS, ICML, ICLR, AAAI, among
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