Director, Oncology Data Science

Director Of Oncology Data Scientist

The Translational Genome Analytics group within the Data, AI & Genome Sciences Department is recruiting a Director of Oncology Data Scientist to join our multi-modal biomarker discovery team. We are seeking an experienced and innovative computational scientist to perform data mining of multi-modal tumor profiling datasets and inform decisions across all stages of our company's expanding oncology pipeline.

The successful candidate will:

• Enable reverse translation from large multi-modal clinical datasets to inform biomarker discovery and combination strategies in molecularly defined patient populations with high unmet medical need.
• Analyze, summarize and visualize the findings from large multi-modal clinico-genomic datasets which include bulk RNAseq, WES/WGS, imaging, epigenetic profiling, single-cell RNAseq, and proteomics data from oncology clinical trials and real-world datasets.
• Leverage advanced deep learning and AI methods and multivariate predictive modeling to discover novel biomarkers predictive of clinical outcomes.
• Effectively collaborate with AIML teams working on development of foundation models trained on large cohorts of human data.
• Present data analyses to inform and interact with stakeholders representing a wide span of internal organizations, including early discovery, translational and clinical development teams.

Qualifications:

Education:

• Ph.D. in quantitative discipline such as Engineering, Applied Physics/Mathematics, Bioinformatics, Computational Biology or related field with a significant computational and statistical component and six (6) years of relevant experience in pharma, biotech or academic setting.

Required Experience and Skills:

• Experience in applying computational methods in cancer biology.
• Demonstrated expertise in the application of methods of statistical learning and data mining to the integrative analysis of multimodal, high-dimensional tumor profiling datasets in the oncology and immuno-oncology context.
• Hands-on analysis experience with the application of machine learning algorithms to large clinico-genomic, genetic and immunogenomic real-world and clinical trial datasets.
• Extensive experience and demonstrated expertise to code in scientific computation environments with adoption of best practices for reproducible data analyses.
• Strong communication and presentation skills; ability to guide and influence decisions through use of data-driven hypotheses; attention to detail.
• Independent, flexible and collaborative mindset

Preferred Experience and Skills:

• Demonstrated experience with analysis of large-scale multi-modal data originating from clinical trials and real-world datasets.
• Deep understanding of the major concepts of cancer biology as represented in multi-modal molecular and imaging data.
• Experience with in a matrixed industry environment and ability to effectively collaborate with colleagues from a wide range of disciplines.
• Record of publishing in high profile scientific journals

Required Skills:

Cancer Genomics, Cancer Research, Computational Methods, Data Science, High Dimensional Data Analysis, Immuno-Oncology, Machine Learning (ML), Multimodal Analysis, Oncology, Real World Data, Statistical Learning

Preferred

Skills: