Head - Data Science - Drug Discovery/AI & ML

Experience Required:

- 10-20 years of work experience in Data Science / Advanced Analytics, experience in creating organization wide capabilities in Data Sciences preferably in Life Sciences / CRO / CDMO / Pharma setups

- 10 years+ of relevant drug hunting experience

- Expertise in cheminformatic and bioinformatic data analysis and large-scale data handling.

- 5 to 8 years of experience in Experience in statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets

- Experience in Cloud Service Operations of Azure / AWS especially in Analytics area

- Experience in sizing the right teams to deliver the work, operations etc., and productizing Data Science solutions

- Application Development background along with knowledge of Analytics libraries, open-source

Educational Qualifications:

- Ph.D. in Cheminformatics, Computational Chemistry, Computational Biophysics, Bioinformatics or related field

- Demonstrated impactful contributions to small-molecule drug discovery programs as exemplified by publications and/or patents/patent applications.

- Post-Graduation in Data Science / Advanced Analytics (AI / ML) from a Premier Institute is required. Doctorate in Data Science with focus on Drug Discovery is an added advantage

- Certification in Big Data Technologies Analytics Cloud Platform (Azure or AWS) is an added advantage

Descriptions:

- Develop Data and end to end Data Science Strategy for AI / ML enabled Transformation.

- Work cross-functionally with colleagues in Medicinal Chemistry, Structural Biology, Biophysics, Pharmacology, ADME/Tox, DigitalX and other functions within interdisciplinary drug discovery teams

- Experience in a wide range of computational chemistry techniques including similarity searching, SAR modeling, molecule enumeration, ligand-protein modeling and the application of modern machine learning tools to key problems in chemistry, biology, and drug design.

- Champion and actively promote the use of Cheminformatics, Bioinformatics, Data analytics and AI/ML methods to expedite the Design-Make-Test-Analyze discovery cycle

- Evaluate emerging computational and drug modalities and their application to drug discovery workflows