• Perform analysis of clinical and biomarker datasets (e.g., large-scale -omics datasets including RNASeq, and Whole Exome/Genome Sequencing) and derive clinically meaningful interpretations.

• Develop and apply computational methods for patient stratification based on integration of molecular profiling datasets.

• Drive in-depth analysis of stratified patient data to elucidate biological and driver mechanisms in key oncology indications (integrating multiple omics datasets, including scRNAseq and spatial transcriptomics).

• Work with colleagues in the Informatics Predictive Sciences group to identify drivers of disease heterogeneity and potential therapeutic targets for patients in unmet need.

• Collaborate with experimental teams in the iterative validation of therapeutic and mechanistic therapeutic hypotheses.

• Summarize analysis results and report conclusions to decision-making bodies.

• Ph.D./ M.S./Btech/ or any other life science with 2+ years of experience in computational biology, bioinformatics, statistics or related fields. Experience in biotech or pharmaceutical industries is a plus.

• Strong experience in the use of a high-level programming language such as R, MATLAB, Python or Perl for complex data analysis and reproducible research practices is a must.

• Substantial experience working with Next Generation Sequencing datasets, including RNAseq, scRNAseq, Whole-Genome, Whole-Exome Sequencing and Spatial Transcriptomics.

• Familiarity with contemporary cloud and high-performance computing environments.

• Expertise in application of contemporary machine-learning/AI approaches.

• Ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects while conforming with rigorous requirements of clinical studies.

• Strong problem-solving and collaboration skills, rigorous and creative thinking.

• Excellent communication, data presentation, and visualization skills.

• Fluent written and verbal English language communication.