
Group Head Biologics Virtual Screening – Large Molecules Research
- Barcelona
- Permanente
- Tiempo completo
- Location: Barcelona
- 50% Remote working and up to 5% of travel expected
- Job type: Permanent, Full time
- Lead a global team of AI & computational scientists (PhD level) with members from multiple locations (e.g. Germany, Belgium, France, Spain & US) within the AI Innovation department of LMR.
- Drive the implementation & evolution of cutting-edge virtual screening design & biologics property prediction methods for Antibody, NANOBODY® and additional protein-based modalities.
- Provide support to LMR portfolio and innovation projects utilizing the de novo & synthetic library design platform to discover and optimize novel biologics under consideration of resource & timeline planning, external costs, and prioritization.
- Mentor, upskill and develop a PhD-level team of AI scientist & focus on retention of key talents in highly competitive environment.
- Take a leading role for the implementation and execution of the digital strategy in the area of biologics virtual screening, AI-based methods for biologics property prediction and computational multiparametric optimization of complex multi-specific protein therapeutics within LMR.
- Evaluate external landscape, initiate, and drive external partnerships & collaborations for AI-based virtual screening, property prediction and multiparametric optimization.
- Oversee, plan, and manage budget for external collaborations, method validations and project support.
- Lead cross functional and interdisciplinary workstreams as well as external collaborations in the field of biologics virtual screening, property prediction & multiparametric format optimization.
- Manage key internal and external interfaces to key stakeholders and collaborators including LMR wet lab & project teams, Digital R&D, therapeutic units, academia, and external partners.
- Stay abreast of the latest advancements in machine learning, AI and related computational methods for biologics optimization and apply them to enhance the company’s competitive edge.
- Represent Sanofi at external conferences and increase its footprint with publications and patents.
- Experience:
- Ph. D. in a field of computational life sciences, AI/ML or computational protein design (e.g. bioinformatics, computer science, biophysics, computational biology, or life sciences with strong expertise in data science).
- Outstanding expertise in latest AI-based & computational methods for protein design, property prediction and multiparametric biologics optimization.
- Strong experience working in the field of computational Antibody/NANOBODY® engineering in a Biopharma setting including interactions and coordination of interdisciplinary project and wet lab teams.
- Strong track record in method development for protein property prediction & computational protein engineering.
- Strong publication and/or patent track record in the field of ML/AI-driven computational protein design & optimization.
- Solid understanding of biophysical and biochemical methods for characterization of protein-based therapeutics.
- Soft skills:
- Passion to collaborate with others, self-development with a focus on scientific know-how, sharing knowledge, results orientated ways of working and talent development.
- Leadership skills to work international and cross-functional in and outside of the department and organization.
- Demonstrated ability to influence stakeholders, to communicate effectively and to work in a global and matrix organization.
- Background in project management (timelines, budget, project updates and strategies)
- Experience in leading, upskilling and developing a team of experts.
- Strategic thinking and long-term planning ability.
- Excellent communication and presentation skills including ability to successfully interface with senior management.
- Ability to make timely decisions and to defend the needs of the section and department.
- Open mindset of act for change by challenging current status.
- Technical skills:
- Strong expertise in latest state of the art methods for AI-based computational protein design & optimization including protein language models, geometric deep learning approaches, multiparametric optimization and generative sequence/structure generation.
- Deep understanding on of multi-specific protein formats (Antibody- & NANOBODY®-based) including structural components and the efficient exploration of huge design spaces under consideration of multiple optimization criteria.
- Deep expertise with protein structure or sequence featurization/embeddings and protein language models.
- Knowledge of antibody/NANOBODY® structure, function & engineering.
- Proven experience on developing and applying computational methods for the prediction of antibody/NANOBODY® biophysical properties and multiparametric optimization.
- Proficiency in Python and related libraries for machine & deep learning, data science and protein design.
- Ability to develop, benchmark and apply predictive algorithms for protein property prediction and virtual screening.
- Experience in handling, curating, and managing large biological data sets and data bases.
- Expertise in developing and maintaining computational pipelines and automation of workflows.
- Comfortable working in cloud and high-performance computational environments.
- Languages:
- Fluent in English, oral and written.