Data Scientist - I (Assistant)

Data Scientist - I (Assistant)

Data Scientist Rahway, NJ Work Schedule: Hybrid role requiring 3 days onsite and allowing for 2 days of remote work per week.

Job Description

Data Scientist Rahway, NJ Work Schedule: Hybrid role requiring 3 days onsite and allowing for 2 days of remote work per week. Key Skills: Python for scientific computing (NumPy, PyTorch, etc.). High performance computing in a Unix/Linux OS environment. Cloud computing, e.g., AWS experience Machine Learning experience large scale chemical foundation models and/or protein structure related models. Pay rate: Up to $55/hr Qualifications: Bachelor in Computational Physics/Chemistry, Bioinformatics, Computer Science or another relevant quantitative field OR Master's and PHD in Computational Physics/Chemistry, Bioinformatics, Computer Science or another relevant quantitative field Years of experience: 0-3 years. Skilled in Python for scientific computing (NumPy, PyTorch, etc.). Experience with high performance computing in a Unix/Linux OS environment, or familiarity with cloud computing, e.g., AWS experience Hands-on data science/machine learning experience. Working knowledge of biochemistry and/or chemistry. Nice to have: Experience with research projects in the pharmaceutical early discovery space. Experience with cutting-edge machine learning applications and are motivated to jump in to see their application to drug discovery. Experience with developing large scale chemical foundation models for virtual screening applications and/or protein structure related models. Responsibilities: Become part of a cutting-edge and innovative Quantitative Biosciences department. Using robust scientific methodology, novel translational models, state-of-the-art instruments, and automated equipment, we engage in multidisciplinary collaborations to discover the next medical breakthrough. We have expanded our capabilities to drive higher-resolution interrogation of novel biology and the molecular mechanism of action of molecules that help advance the best drug candidates to the clinic. Individuals joining us will have the opportunity to address challenging data problems, across multiple modalities with a hands-on influence in delivering solutions directly to biologists and chemists designing the next generation of medicines. We operate in a truly multicultural, collegial, and collaborative work environment that is rich in development and growth. The Opportunity As a Data Scientist within the Quantitative Biosciences Department at Merck, you will lead statistical analyses and research data science solutions for small molecule and peptide workflows in early drug discovery. In the Quantitative Biosciences department, you will have the opportunity to explore the complex data output of cutting-edge high-throughput experimentation from across the organization. You will apply rigorous computational methods to help scientists generate novel and impactful hypotheses for target discovery. The Data Scientist will also provide stakeholders with an in-depth understanding of the data and interpretation of analytical results, providing expertise during the experimental development process. This is an ideal opportunity for candidates who are intellectually curious, want to work on the interface of computational science and drug discovery and look to grow in their next role. You should be able to communicate results effectively and concisely to stakeholders, both orally and in written format, balance priorities and build strong relationships among multidisciplinary cross-organizational teams. As a Data Scientist, you will: Develop new machine learning workflows, including deep learning workflows, that enable the discovery of new medicines. Contribute to property prediction models for small molecules. Collaborate with interdisciplinary discovery teams to apply a variety of informatics and modeling techniques to identify new drug discovery leads and accelerate the evolution of leads to drug candidates. Conduct research in areas of machine learning and molecular modeling relevant to drug discovery.