Associate Principal Scientist - Computational Biology/Data Science, AI/ML

Job Description

The Translational Genome Analytics group within the Data, AI & Genome Sciences Department is recruiting an Associate Principal Scientist to join our oncology data science team. We are seeking an experienced and innovative computational scientist to impact our company’s vast and expanding oncology pipeline with a focus on immunotherapy, including personalized neoantigen approaches. The successful candidate will:

• Analyze, visualize and summarize NGS and other molecular data from oncology clinical trials and proprietary and public large molecular databases, including bulk tissue nucleotide acid sequencing and periphery comprehensive molecular assays including TCR sequencing, protein profiling, immune activation assays, and liquid biopsies.

• Leverage cutting-edge AI/ML (e.g. classification and regression algorithms) and integrative analytic methods to investigate proprietary and public immunogenomic datasets in conjunction with tissue and periphery assays with the goal of improving the detection and utilization of patient-specific neoantigens.

• Use oncology molecular data to identify biomarkers and molecularly defined groups of patients as well as molecular mechanisms of modulation of therapeutic response reflected in longitudinal analytes, with a focus on identifying novel targets matched to groups of cancer patients with unmet medical need.

• Effectively present data analyses to inform and interact with stakeholders representing a wide span of internal organizations, including Translational Oncology, Molecular Diagnostics, Clinical Development, Statistics and Therapeutic Area Biology.

Education Minimum Requirement:
PhD (preferred) in Engineering, Applied Mathematics, Bioinformatics, Computational Biology or related field with a significant computational and statistical component and 3+ yrs post-PhD experience or MSc with 10+ yrs experience in applying computational methods in cancer biology in a pharma, biotech or academic setting.

Required Experience and Skills:

• Demonstrated expertise in the application of methods of statistical learning and data mining to the integrative analysis of high-dimensional molecular profiling datasets in the immuno-oncology context 

• Hands-on analysis experience with algorithms for large genetic, genomic, immunogenomic and clinical datasets (e.g. IEDB, TCGA, GTEx, DepMap)

• 5+ years experience and demonstrated expertise to code in scientific computation environments (R/Python, Matlab) 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:

• Experience with analysis of genomic data originating from clinical trials.

• Understanding of the major concepts of cancer biology as represented in molecular data.

• Experience with a matrix environment and ability to effectively collaborate with colleagues from a wide range of disciplines.

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