Director Machine Learning Research (m/f/d)

The Head of Machine Learning Research (m/f/d), within R&D Data Science & AI organization will be responsible for developing and applying cutting-edge AI / machine learning techniques for value generation across the Pharma R&D pipeline. An ideal candidate should be a renowned leader in machine learning research and its application to the life sciences. The incumbent (m/f/d) will increase visibility of the work of the group across Bayer, while keeping the focus of the group and being able to prioritize projects that involve a real machine learning component. This function (m/f/d) will work collaboratively with computational scientists, biologists, chemists, and clinicians across R&D, as well as other teams within DS&AI organization such as data science translators or the applied machine learning group, to create a pipeline of state-of the art machine learning tools addressing the most pressing needs in drug discovery and development.

YOUR TASKS AND RESPONSIBILITIES

• Identify most impactful and toughest challenges of drug discovery and solve them at scale via development of novel machine learning algorithms leveraging internal and external data sources
• Be responsible for development and effective execution of the MLR strategy to enhance R&D productivity and speed
• Collaborate and interact closely with stakeholders across the PH R&D and external partners to progress projects through different stages
• Go beyond buzzwords and identify novel techniques to challenge internal ways of working/development to have an impact on value creation for the R&D pipeline through the use of machine learning
• Remain at the cutting edge of ML research by keeping up to date with the latest techniques and approaches in machine learning and statistical analysis by actively engaging within the broader ML research community
• Publish high impact open-source software and publications at the major ML conferences, contribute technical blogs and forge strategic alliances with academia and other industry, ultimately enhancing Bayer’s reputation and presence in the biopharma industry
• Grow, develop and lead a team of machine learning researchers to successful project delivery using modern software development methodologies and collaboration tools. Mentor and develop more junior AI/ML researchers. Ensure that teams are adhering to established standards, coding practices, and way of working

WHO YOU ARE

• PhD or equivalent experience in machine learning or a related quantitative field (e.g. Computer Science, electrical, Statistics, Mathematics, Physics, Computational Biology/Chemistry) with long-term relevant experience, including several years in the pharmaceutical industry
• Demonstrated track record of executing research program that publishes in top-tier ML national or international conferences. Examples include but are not limited to: NeurIPS, ICML, UAI, COLT, AAAI, ICLR, JMLR.
• Experience in analyzing complex datasets and building mathematical models, also in a practical context and preferably across pharma R&D. Deep understanding of end-to-end R&D processes and data
• Extensive knowledge of machine learning from a theoretical and practical perspective, with profound knowledge in deep learning.  Interest in representation learning, generative modeling, reinforcement learning, computer vision, natural language processing, statistical learning theory, statistics, and/or causal inference
• Expertise with tools and libraries for ML including PyTorch, TensorFlow, and JAX. Excellent programming skills in one or more languages, with an intimate knowledge of the Python scientific ecosystem
• Understand and recommend infrastructure needs to enable Bayer to be highly competitive in Machine Learning. Experience with HPC, on-prem and cloud computing environments and the computational needs of modern machine learning models
• Empathetic leadership with high impact, partner and influence across complex matrixed organizations and manage delivery to diverse stakeholder groups. Ability to distill complex issues and clearly articulate solutions
• Comfortable explaining complex analytical and technical content to audiences with various knowledge baselines
• Fluent in English, both written and spoken

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