Job Description :
The successful candidate will work closely with stakeholders in the Quantitative Medicine and Genomics (QM&G) organization to develop predictive AI / ML models using large-scale transcriptomic and imaging datasets to elucidate drug mechanism of action (MOA). Key responsibilities can include ingesting public data, performing data clean-up to ensure model compatibility, integrating external and internal datasets, and optimizing multi-model models. This role offers the opportunity to make a direct impact on drug development by delivering innovative data science solutions across cross-functional projects.
Responsibilities :
- Support senior analysts in implementing, training, and troubleshooting AI models.
- Ingest, clean, and preprocess large-scale transcriptomic and imaging datasets for AI workflows.
- 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.
Requirements :
MS degree (+ years of experience) or PhD ( 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) Proficiency in Python and standard ML libraries.Proficiency working on HPC or cloud environments.Domain knowledge in bioinformatics / computational biology.Strong attention to detail, documentation, and communication skills.Ability to independently execute and troubleshoot research plan.Preferred skills :
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.Top skills :
Implementing, training AI / ML models.Data pre-processing for AI compatibility.Domain knowledge of bioinformatics.Experience working with genomic data.Ability to communicate results clearly.