Job Description Summary
Director of Applied AI Research | Data Science (Drug Discovery)Job Description
Key responsibilities
Have a deep and broad understanding of AI/machine learning (ML) methods
Have a deep understanding of drug discovery and relevant AI/ML methods for in-silico/3D molecule and protein design, retro/synthesis, molecular dynamics, quantum chemistry…etc.
Lead efforts in AICS surrounding Generative Chemistry
Take a hands-on role and deliver on highly visible multiple projects
Serve as an ambassador for AI & Data Sciences at Novartis by presenting and publishing articles
Actively build and lead engagements with academia, technology, and industry partners (e.g., Microsoft Research)
Keep ahead of latest developments in the field and mentor associates
Be a part of a truly unique organization with an inter-disciplinary team made up of highly accomplished scientists who are curious, love to learn, and push the AI frontier in drug discovery
Role Requirements
An advanced degree (master’s or doctoral) in a quantitative field including but not limited to Computer Science, Applied Mathematics, Astro/Physics, Computational Chemistry, all relevant engineering and science disciplines
9+ years of relevant thought leadership/publication track record in machine learning applications and AI/ML methods
12+ years of experience in end-to-end analysis for large scale data sets
Cross-industry experience highly preferred and previous experience in pharma, biotech, or healthcare desired.
Novartis is committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.
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