Senior Expert I, Data Science

Job Description Summary

Position Location: onsite, San Diego, CA

#LI-hybrid

This role is based in San Diego, CA. Please only apply if this location is accessible for you.

The Oncology Data Science group within Biomedical Research supports the Oncology Disease Area with computational biology, Artificial Intelligence / Machine Learning (AI/ML), and data engineering for novel therapeutics across multiple drug modalities. As integrated scientists and engineers, we apply advanced analytics to pre-clinical and clinical projects, enabling progress in target discovery, drug development, and translational and clinical science.

Help us bring innovative drugs to the clinic by analyzing and interpreting multi-dimensional molecular data (‘omics) into target identification, drug development, and patient biomarker discovery.

The Low Molecular Weight (LMW) team at Novartis Biomedical Research Oncology Data Science is seeking a highly motivated Senior Computational Scientist to join our team. With a focus on induced proximity therapeutics, you will collaborate with cross-functional teams in Biomedical Research and Oncology stakeholders to advance efforts in target identification and drug development to support our ground-breaking drug discovery programs.

Job Description

Major accountabilities:

• Collaborate closely with interdisciplinary wet-lab and computational scientists to design, analyze, and interpret high-dimensional biological data (e.g., bulk RNA-seq, DNA-seq, CRISPR, drug screening) to inform critical project decision.
• Lead profiling strategies and analysis of high-throughput genomic and phenotypic screening data to inform patient stratification and mechanism of resistance, in support of drug discovery and development.
• Integrate multi-modal internal and external preclinical datasets (e.g., genomics, transcriptomics, pharmacology, and functional screens) to produce translationally relevant insights.
• Apply advanced bioinformatics and machine learning approaches across multi-modal datasets to uncover novel, actionable biological insights and therapeutic hypotheses.
• Develop and implement innovative analytical methods to support emerging technologies and to effectively integrate, interrogate, and visualize multi-dimensional datasets.
• Drive oncology research by leveraging data mining and genomic profiling to identify novel targets for induced proximity modality, elucidate mechanism of action and support patient stratification strategies.
• Communicate integrative analyses and key findings clearly and effectively to diverse audiences, including cross-functional scientific teams and stakeholders.

Qualifications:

• PhD in Computational Biology, System Biology, Bioinformatics, Data/Computer Science, or related field with relevant industry experience.
• Strong knowledge of cancer biology and multi-modal data types such as genomics, transcriptomics, proteomics and phenotypic screening data.
• Proficiency in one or more programming languages for bioinformatics applications (e.g., Python, R) with experience in UNIX/Linux environment, version control, and reproducible workflows.
• Demonstrated statistical rigor and analytic depth in the analysis of high-dimensional omics datasets (e.g., bulk and single-cell transcriptomics, genomics).
• Demonstrated experience leveraging AI‑assisted coding tools (e.g., copilots, code generators, and LLM-based workflows) to accelerate data analysis, model development, and reproducible scientific pipelines.
• Familiarity with data workflows, including preclinical biomarker discovery and validation; survival analysis is a plus.
• Proven ability to work independently, prioritize tasks effectively, define next steps and manage multiple projects and stakeholders in a fast-paced environment.
• Excellent communication skills, with the ability to deliver complex scientific concepts to diverse audiences.
• Curiosity, creativity and a solution-oriented mindset when addressing scientific problems.
• Fluency in English (written and verbal).