Develop predictive AI/ML models using large-scale transcriptomic and imaging datasets to elucidate drug mechanism of action (MOA) for the Quantitative Medicine and Genomics (QM&G) organization.
Requirements
- Proficiency in Python and standard ML libraries
- Proficiency working on HPC or cloud environments.
- Experience with NumPy, Pandas, Scikit-learn, Matplotlib, and seaborn.
- Experience with TensorFlow, and/or PyTorch
- Experience with git for version control and collaboration
- Experience with OpenCV, Scikit-image, and computer vision deep learning models
- Experience with multi-modal models
Responsibilities
- Ingest, clean, and preprocess large-scale transcriptomic and imaging datasets for AI workflows.
- Support senior analysts in implementing, training, and troubleshooting AI models.
- Collaborate with scientific and technical teams to translate biological questions into computational solutions.
- Document processes and outcomes ensuring reproducibility and transparency.
- Assist in interpreting model outputs to advance the understanding of drug MOA.
- Work with agility on time-bound projects with critical deliverables.
- Optimizing multi-model models.
Other
- MS degree (5+ years of experience) or PhD (0 years of experience) in a quantitative field (Bioinformatics, Computer Science, Computational Genetics, Biostatistics, AI/ Machine Learning Engineer or other field with a strong quantitative and computational background)
- Domain knowledge in bioinformatics/computational biology
- Strong attention to detail, documentation, and communication skills
- Ability to independently execute and troubleshoot research plan
- CO/NYC candidates might not be considered
