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Head of Machine Intelligence, Ai
2 weeks ago
**Job title**:Senior Director, Head of Machine Intelligence
**Location**:Gothenburg (Sweden), Cambridge (UK), Gaithersburg (USA), Barcelona (Spain), Warsaw (Poland), Canada
**What you**’**ll do**
Machine intelligence explores virtually all aspects of machine learning, including deep learning, representation learning, reinforcement learning, active learning, statistical learning theory, causality, learning with graphs and structured data, and ranking & recommendation problems. In this role, you will lead and manage multiple groups of machine learning scientists and engineers with the responsibility to deliver strategic priorities in AI and machine learning. This will include contributing with novel ideas, design and execution of machine learning projects, ensuring that the teams adhere to the latest methods and engineering best practices and way of working. You will also be expected to define your own research agenda and collaborate with the Head of Research to drive forward a cohesive program of basic, fundamental, and applied research. The role gives you an opportunity to realize your ideas in products and services used in clinical trials and across the drug development pipeline.
Accountabilities
- Planning of work and long-term development of the department in close collaboration with your manager and other leaders in the team
- Coach, mentor, motivate and inspire your team towards enabling projects and setting research direction
- Build and maintain a work environment in which scientific challenge and innovation is central
- Propose, establish and manage external partnerships
- Ensure that the work produced by your department is performed with high quality in accordance with coding standards
- Contribute to the leadership of the Center for AI, including design and execution of the CAI strategy
- Provide thought leadership on modern AI and ML engineering practices in the Center for AI and the broader AZ community
- Communicate the strategy of Center for AI to a wide variety of internal and external partners
- Continue to actively engage in ML research with and through the broader ML research community. Publish papers at the major ML conferences, contribute technical blogs and forge strategic alliances with academia and other industry
- Remain at the cutting edge of ML research, especially in a chosen subfield (e.g., deep learning, generative models, causal inference, reinforcement learning, Bayesian methods, active learning).
- Manage academic partnerships
- Externally regarded as an expert in Machine Intelligence within the community
**Essential **for the role**
- PhD degree in computer science, electrical engineering, statistics, applied mathematics, or a related field.
- At least 8 years of experience in developing machine learning models (a minimum of 3 years industry experience after completion of PhD studies).
- ML Ops experience: model tracking, model governance, multiple models in different production contexts
- End-to-end experience leading collaborative ML/data science projects in an industry setting.
- Demonstrated an outstanding track-record of delivering a portfolio in a highly regulated setting.
**Desirable for the role**
- A research program demonstrated by 8 or more journal and conference publications in prestigious venues (with at least 4 publications as a leading author). Examples include but are not limited to: NeurIPS, ICML, UAI, COLT, AAAI, KDD, ICLR, EMNLP, WWW, JMLR, Ann Stats. Leadership in a high impact model deployment (e.g., developed over a period of 4+ years) or 4+ small-to-medium scale deployments.
- 3+ years of experience within the pharmaceutical industry with either extensive knowledge of clinical trial process (all phases) and associated regulatory activities or the discovery pipeline and accompanying investment decisions.
- Deep understanding of drug development and clinical trial process and data (all phases).
- Participation and activity in the scientific community on program committees, editorial boards, organizing workshops, etc.
- Demonstrated leadership potential in research and capability in expanding the frontier of addressable problems.
- Demonstrated experience in at least three of the following research areas: foundational research in machine learning, deep learning, generative models, causality, natural language processing, graphs and structured data, active learning and optimization.
- Track record of collaborating successfully with AI engineering teams to deliver complex machine learning models.
**Why AstraZeneca?**
At AstraZeneca we’re dedicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on an exciting journey to pioneer the future of health
