Technical Director (Scientific Innovation), AI and Computational Sciences (AICS)

Job Description Summary

Novartis has embraced a bold strategy to drive a company-wide digital transformation. Our objective is to position Novartis as an industry leader by proactively adopting digital technologies that foster innovative approaches to hasten drug discovery and development. By utilizing both internal and external R&D data with the power of data science, predictive models, generative AI, and machine learning, our objective is to identify new targets, create more effective therapeutic molecules, better predict drug pharmacokinetics and safety risks, refine clinical trial design, and significantly shorten development cycles. The AICS team leads BR in exploring and applying advanced AI and ML methodologies to generate novel drug discovery insights, and to speed and improve drug discovery efficiency whilst focusing on patients’ needs.

AICS partners with drug discovery teams, raises the level of AI expertise across Biomedical Research (BR) and ensures that BR science keeps up with the rapidly evolving ecosystem of AI technologies by connecting with AI leaders in academia and industry.

This role within the Scientific Innovation group of AICS will be tasked with providing functional leadership of a cross-functional group of scientists including AI experts and for the BR priority projects. Additionally, the role would be expected to act as the SME liaison of AICS to BR Disease Areas (DAs) and Function Areas (FAs).

 

Job Description

Purpose of the role 

Scientific Innovation Technical Director: 

 

• Lead a team of AI researchers and SME with core domain expertise in applying AI to multiomics, disambiguation of gene-regulatory networks and genetics 

• Work with AI modelers to ensure models are robust and performant 

• Work with Engineering and Product Development resources to ensure delivery of AI solutions 

• Raise the awareness of AI applications to aid in answering key research questions across BR 

• Help position AI-aided drug discovery contributions to deliver and support progress of BR’s portfolio, enable new kinds of therapeutic discoveries, shorten cycle times, and increase efficiency.  

Collaboration & partnership 

 

• Act as liaison of AICS for identification of research questions, solution design, and evangelism of results and impacts of AI across BR 

• Regularly communicate, engage, align with AICS teams, broader data science community, and senior scientists.  

• Initiate and lead key high-value internal collaborations across BR. 

• Help to design translatable metrics for AI models that will lead to tangible impact in collaboration with BR DAs and FAs. 

What you’ll bring to the role: 

• 10+ years of significant experience in innovation, development, deployment and continuous support of Machine Learning data management and modeling 

• Specialization in multi-omics, gene-regulatory networks, perturbation models and the like 

• Expertise in bringing advanced analytics insights and actions to a large Research organization & deep understanding of Drug development 

• Passion for understanding emerging technologies with pragmatic insight into where those technologies can be integrated into business solutions 

• Ability to balance requirements, manage expectations, and drive effective results using a proactive attitude towards identifying and resolving issues 

• Entrepreneurial spirit with can-do, pro-active attitude 

• Strong organizational, problem-solving, and influencing skills and demonstrated track record of exceptional teamwork 

• Excellent interpersonal, communication, and presentation skills 

• Ability to execute and prioritize well in a complex matrixed environment. 

• Functional leadership 

 

 

 

Skills Desired

Applied Mathematics, Artificial Intelligence (AI), Aws (Amazon Web Services), Big Data, Building Construction, Cloud Computing, Computer Science, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Electrical Transformer, Machine Learning (Ml), Master Data Management, Professional Services, Python (Programming Language), R (Programming Language), Random Forest Algorithm, Statistical Analysis, Time Series Analysis