Lead Scientist - Computational Toxicology

About the Department Computational Drug Design (CDD) is the Novo Nordisk organization where we develop and apply state of the art capabilities in data science, predictive modeling, and AI/ML to enable both ligand and structure-based drug design. Scientists within CDD collaborate seamlessly with teams across the globe to invent better molecules faster. With an unbiased approach to drug modalities, in-house access to the newest screening techniques, and world-class experimentation, we have a wealth of opportunities to impact our pipeline and save and improve patient lives. We work in multidisciplinary, highly collaborative discovery teams both locally and across Novo Nordisk’s global network to invent novel medicines by applying advanced computational techniques and developing innovative computational methods. We engage in external collaborations to ensure access to innovative research and technology. Small Molecule Digital Chemistry (SMDC) is dedicated to the research and development of small molecule therapeutics. Our role is two-fold: support cutting-edge pharmaceutical research projects and develop innovative computational technologies in the digital chemistry field. To be successful, we foster strong collaborations with Small Molecule Medicinal Chemistry within our Global Research and Technology (GRT) organization and colleagues across the Data Science and Innovation (DSI) organization. Our mission is to deliver in silico models, cheminformatics tools and computational chemistry platforms to enable model-driven small molecule drug design to benefit patients across the globe. We believe in the value of a diverse and inclusive culture. Together, we build and grow talent to ensure the development of novel solutions. The team is comprised of collaborative, diverse and passionate people who have a true sense of pride in their work. We are committed to helping each other grow, and we are driven by the opportunity to make a difference in the lives of people living with chronic disease. Everything we do starts and ends with the patient. And, while changing the future of chronic disease treatment, such as diabetes and obesity, is no easy task, we also recognize the importance of an enjoyable workplace, which is why we have cultivated a culture of fun, ingenuity, and innovation. Our competitive compensation package and support of a healthy work-life balance all add to a best-in-class employee experience. The Position The Lead Scientist – Computational Toxicology will work closely with colleagues across the R&D organization to develop and implement computational models for toxicological risk assessment, analyze and interpret complex toxicological data and collaborate with cross-functional teams to integrate computational toxicology into drug discovery and development processes. The ideal candidate will have knowledge and experience in toxicology data analysis and modeling, advanced computational modeling methods and pharmaceutical project support. The successful candidate should have working knowledge of common toxicology data sources and analysis tools including AI/ML approaches and physics-based predictive modeling. Please note we are open to hiring at more senior title commensurate with experience Relationships The Lead Scientist – Computational Toxicology reports to the Senior Scientific Director of SMDC. They will have close interactions with scientists and experts across the Computational Drug Design, Medicinal Chemistry, Quantitative Biology and ADMET teams. Additional internal partners include data scientists, data engineers, ML specialists, software developers and other R&D scientists across the US, UK, and Denmark. External relationships include commercial and academic collaboration partners. Essential Functions Develop, evaluate and implement computational models for toxicological risk assessment Build, evaluate and optimize models to predict toxicological outcomes across multiple therapeutic modalities including small molecules, biologics and RNA therapeutics. Apply toxicology modeling tools and principles to facilitate multi-property optimization of therapeutic molecules and advance portfolio projects. Analyze and interpret complex toxicological data using advanced machine learning techniques Process and manipulate structured and unstructured toxicological data from various sources, including in vivo & in vitro PK/PD data, metabolomic, molecular modelling, multi-omics, imaging and text-based datasets. Utilize advanced algorithms and approaches including AI/ML, mathematical and statistical methods to identify key toxicological features and patterns from large multimodal datasets Apply natural language processing (NLP) and image analysis techniques to extract relevant information and insights from scientific literature and images Employ graph theory and network analysis to understand relationships between chemical structures, biological pathways, and toxicological effects Collaborate with cross-functional teams to integrate computational toxicology into drug discovery and development processes Work closely with toxicologists, chemists, pharmacologists, and other stakeholders as the computational toxicology lead on portfolio programs Provide toxicological expertise and insights to guide decision-making on assets in pre-clinical and clinical development Communicate findings and recommendations to diverse audiences, including presentations to internal teams and external partners as well as scientific publications and presentations. Physical Requirements Up to 20% overnight travel required. Qualifications Master’s degree required, PhD strongly preferred. Degree within in Toxicology, Computational Chemistry, Computational Biology, Bioinformatics, or related quantitative field Master’s degree with 5 years’ relevant experience, or PhD with 4 years’ relevant experience Relevant experience and skills include: Research experience in the field of drug discovery for evaluating product safety using computational toxicology methods within industry or academia (industry preferred). Solid understanding of toxicology principles and mechanisms and deep understanding of predictive biology. Proven track record of knowledge and use of applied toxicology, toxicity-testing methodologies, dose-response analysis, and safety risk assessment. Ability to perform in-depth data analysis and draw meaningful conclusions from complex data. Experience analysing large, complex datasets in a high-performance compute environment using mathematical, statistical and AI/ML tools. Excellent written and oral communication skills, with an emphasis on presentation abilities Preferred experience and skills include: Hands on experience in developing, maintaining and deploying proprietary global and local computational toxicology models in a corporate environment. Working knowledge of popular commercial in silico toxicity platforms, data sources, and tools. Familiarity with regulatory requirements and guidelines related to safety assessment and toxicology. We commit to an inclusive recruitment process and equality of opportunity for all our job applicants. At Novo Nordisk we recognize that it is no longer good enough to aspire to be the best company in the world. We need to aspire to be the best company for the world and we know that this is only possible with talented employees with diverse perspectives, backgrounds and cultures. We are therefore committed to creating an inclusive culture that celebrates the diversity of our employees, the patients we serve and communities we operate in. Together, we’re life changing. Novo Nordisk is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, gender identity, sexual orientation, national origin, disability, protected veteran status or any other characteristic protected by local, state or federal laws, rules or regulations. If you are interested in applying to Novo Nordisk and need special assistance or an accommodation to apply, please call us at 1-855-411-5290. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.