Scientist II - Computational Biology, Pharma R&D

Role Overview The Scientist II will join the Computational Biology, Pharma R&D team to analyze large-scale molecular and clinical datasets, supporting drug discovery and development. They will collaborate with pharmaceutical partners to execute computational research strategies, integrating diverse data types such as genomic, transcriptomic, imaging, and clinical information to generate actionable insights.

Key Responsibilities

• Partner with external collaborators to address research questions using the platform’s multimodal data.
• Perform robust analyses, applying statistical and computational methods to inform clinical trial design, biomarker discovery, and understanding disease biology.
• Leverage AI, including Large Language Models and agentic workflows, to accelerate research workflows and insight generation.
• Develop, evaluate, and implement new analytical methods for real-world and omics datasets, contributing to reusable code and best practices.
• Work cross-functionally with research, clinical, data science, and engineering teams to refine solutions.
• Communicate complex findings effectively to technical and non-technical stakeholders, preparing reports and scientific materials.

Qualifications & Skills

• PhD in Computational Biology, Bioinformatics, Biostatistics, Machine Learning, or related field, or master’s with relevant experience.
• Proficiency in Python and/or R, with experience in computational biology libraries.
• Experience with machine learning, LLM-based coding tools, and agentic frameworks.
• Knowledge of good software engineering practices, SQL, and large relational databases.
• Strong statistical background and understanding of cancer biology or human disease mechanisms.
• Experience analyzing large-scale biological data, such as genomics and transcriptomics.
• Excellent communication skills and ability to work in a dynamic environment.

Preferred Skills

• Experience with generative AI, real-world evidence, survival analysis, causal inference, or systems biology.
• Published research or conference presentations.
• Understanding of drug discovery processes from target to clinical development.