We are looking for a candidate with strong computational, statistical, and biological capabilities and a demonstrated track record of translating complex, multi-modal data into testable hypotheses and actionable insights in support of clinical development activities and decisions.
You will drive exploratory and confirmatory analyses (both hypothesis-generating and hypothesis-driven), across diverse data types generated in drug development, including clinical trial data, genomics, proteomics, imaging, flow cytometry, and other biomarker modalities. You will define and implement approaches, processes, algorithms, and pipelines that support the analytics, visualization, and decision support needs of drug development scientists and project teams, while collaborating closely with Biostatistics leads, Translational and Clinical Scientists, and cross-functional partners across the organization.
Key Qualification, Experience and Skills Requirements;
Ph.D. in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, Computer Science, or related field) and 1+ years of academic/industry experience; or Master's Degree in a relevant quantitative field and 3+ years of industry experience
Experience in the application of AI/ML and proficiency in Python, R, SQL, and cloud platforms (e.g., AWS, Azure, Databricks)
Experience with genomics, proteomics, imaging, flow cytometry, or immunobiology datasets from clinical trials is highly preferred
Outline of Daily Key Responsibilities;
- Data Science & Analytics
- Data Engineering & Reproducibility
- Collaboration & Technical Contribution
