Remote Scientist III

37566110 Remote Scientist III 12 Months Sigma Systems is currently looking for a Remote Scientist. Responsibilities: As an active team member of the Computational Innovation department at Boehringer-Ingelheim, the successful candidate will contribute to oncology drug discovery research through in silico data driven approaches. You will leverage multi-modal omics data analysis and collaborate with biologists to solve scientific challenges to advance drug discovery programs. This opportunity can be remote Support Oncology drug development efforts Identify, learn and apply emerging technologies in data science and its applications to Client cancer therapeutics discovery and development. Apply and develop innovative analysis approaches when standard methods are not adequate Identify and process publicly available and internally generated datasets using statistical and bioinformatics techniques to create meaningful biological insights Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field Interpret, report, and present analysis results, with a high level of integrity and ethics, to biologists and collaborators. Ensure FAIR data analysis with clear documentation and reproducibility Requirements: PhD degree from an accredited institution with experience in computational sciences or a related scientific discipline (e.g., Computer sciences, Computational Biology, Genomics, Biostatistics, Bioinformatics and Biological Sciences) Programming experience with Python and R for bioinformatic data analysis in unix-like systems. Proficiency in working with bulk and single cell NGS data Experience with any of these topics is a plus: oncology or immunology knowledge, spatial transcriptomics, methylation, or liquid biopsy data analysis, public oncology database datasets (TCGA, GTEX, Human cell atlas, CZ CELLxGene Client, Human tumor atlas network, etc) Proficiency in working with high-performance computing clusters (HPC) Proficiency in biological pathway analysis