
location_on310, Elm Avenue, Rahway, Union County, New Jersey, 07065, United States
Join a cutting-edge and innovative Quantitative Biosciences department focused on advancing drug discovery through computational science, high-throughput experimentation, and multidisciplinary collaboration. We apply robust scientific methodologies, translational models, and advanced automation to better understand biological systems and molecular mechanisms, enabling the development of next-generation medicines.
As a Data Scientist within the Quantitative Biosciences team, you will work on complex, high-dimensional biological data across multiple modalities, directly supporting biologists and chemists in early-stage drug discovery. This role is ideal for candidates who are intellectually curious and interested in the intersection of machine learning, computational science, and drug discovery, with opportunities for growth in a highly collaborative research environment.
You will develop machine learning and deep learning workflows to support novel drug discovery, building and improving property prediction models for small molecules and peptides. Your work will involve analyzing complex, high-throughput experimental datasets and conducting research in machine learning, molecular modeling, and computational biology. You will collaborate with interdisciplinary teams to apply computational and statistical methods for drug target identification and lead optimization, while communicating insights and analytical results clearly to scientific stakeholders both verbally and in writing.
This is a hybrid position based in Rahway, NJ, requiring 3 days onsite and 2 days remote.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Work model: Hybrid
310, Elm Avenue, Rahway, Union County, New Jersey, 07065, United States
Rahway, New Jersey
Experience in pharmaceutical early drug discovery research. Exposure to advanced machine learning applications, especially in drug discovery contexts. Experience developing large-scale chemical foundation models, virtual screening models, or protein structure prediction models.
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Skills: Python, Numpy, Pytorch, Scipy, Pandas, High-Performance Computing, Unix, Linux, Aws, Data Science.
Education: Minimum Bachelor's degree in Computational Physics, Chemistry, Bioinformatics, Computer Science, or related quantitative field; Master's degree in Computational Physics, Chemistry, Bioinformatics, Computer Science, or related disciplines (Advanced Degree); PhD in Computational Physics, Chemistry, Bioinformatics, Computer Science, or related disciplines (Advanced Degree).