Improving the health and well-being of people around the world through innovative research, discovery, and development of life-saving medications and vaccines at Merck & Co., Inc.
Requirements
- Ph.D. in Biology, Pharmacology, Data Science, Biostatistics, Computational Biology/Chemistry or other related life sciences field; or a Master’s degree in the same field with 3+ years' experience in some aspect of computational drug discovery or machine learning in a pharma or biotech setting.
- An understanding of pharmacology experiments for the purposes of drug discovery, and how to design statistically sound experiments that will help drive pipeline decisions.
- A background in statistics and machine learning.
- Experience with databases, database queries, scripting, programming and/or the use of statistics or analysis software including SQL, Prism, Python or R.
- An ability to wrangle structured and unstructured data for use in downstream data analyses.
- Experience with multiple data modalities, e.g. genomics data and/or chemical structure data, and insights on how to integrate them with other data formats and perform interpretations.
- A robust understanding of drug discovery and clinical development and experience on drug discovery programs from at least lead identification through to the delivery of a preclinical candidate.
Responsibilities
- Work with department pharmacologists to design statistically sound experiments and critically analyze and interpret the data from these experiments to ensure the success of discovery programs in Immunology, Neuroscience and Infectious Diseases/Vaccines.
- Help develop novel data pipelines that move, integrate and analyze diverse sources of biological data that aid in the identification of targets, profiling of therapeutic candidates or improving the chances of translation (e.g., cellular images, flow cytometry, multi-omics, deep profiling).
- Identify, develop and implement data visualization approaches that help drive decision making on program teams and manage resources for the department.
- Help wrangle and analyze unstructured and structured data that will drive new translational strategies for the company.
- Participate in the development and implementation of active learning-based approaches to drug discovery.
- Work with automation engineers and laboratory scientists to develop the infrastructure to collect and move data off of lab-based instrumentation to help facilitate downstream analyses including those implementing AIML methodologies.
- Identify, validate, and integrate new technologies or methodologies to increase efficiency and productivity, drive program progression, and facilitate clinical translation of discovery programs.
Other
- Ph.D. in Biology, Pharmacology, Data Science, Biostatistics, Computational Biology/Chemistry or other related life sciences field; or a Master’s degree in the same field with 3+ years' experience in some aspect of computational drug discovery or machine learning in a pharma or biotech setting.
- The ability to communicate effectively with and relate to a broad range of stakeholders, including senior leaders, peers, direct reports, partner organizations and external collaborators.
- Excellent written and oral communication skills.
- The ability to adapt to changing priorities.
- A passion for innovation and problem-solving.
