Associate Director, Data Science

MSD•Boston, MA

1d•$159,600 - $251,200

About The Position

The Translational Genome Analytics group within the Data, AI & Genome Sciences Department is recruiting an Associate Director (Associate Principal Scientist) to join our data science team. We are seeking an experienced and innovative computational scientist to perform data mining of molecular data and inform decisions across all stages of our company’s expanding oncology pipeline. The successful candidate will enable reverse translation from clinical datasets to inform biomarker discovery, combination strategies, and novel target identification linked to molecularly defined patient populations with unmet medical need. They will 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. The role involves leveraging advanced deep learning and AI methods and multivariate predictive modeling to discover novel biomarkers predictive of clinical outcomes. Demonstrated expertise in the application of statistical inference frameworks and/or deep learning approaches to the integrative analysis of multimodal, high-dimensional tumor profiling datasets in the oncology and immuno-oncology context is required. The candidate will also effectively collaborate with AIML teams working on development of foundation models trained on large cohorts of human data.

Requirements

Master's (with 8+ years) or Ph.D. (with 4+ years) in a quantitative discipline such as Engineering, Applied Physics/Mathematics, Bioinformatics, Computational Biology or related field with a significant computational and statistical component and relevant experience in pharma, biotech or academic setting.
Demonstrated expertise in the application of methods of statistical learning and data mining to the integrative analysis of multimodal, high-dimensional molecular profiling datasets in oncology, immuno-oncology or other therapeutic area.
Hands-on analysis experience with algorithms for large genetic, genomic, immunogenomic and clinical datasets (e.g. IEDB, TCGA, GTEx, DepMap).
Expertise to code in scientific computation environments (R/Python, Matlab) with adoption of best practices for reproducible data analyses.
Demonstrated expertise in the application of statistical inference frameworks and/or deep learning approaches to the integrative analysis of multimodal, high-dimensional disease profiling datasets in oncology, immuno-oncology or other therapeutic area.
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.

Nice To Haves

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.
Record of publishing in high profile scientific journals.

Responsibilities

Enable reverse translation form clinical datasets to inform biomarker discovery, combination strategies, and novel target identification linked to molecularly defined patient populations with 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.