Based in Cambridge, MA, the GI/microbiome computational biology scientist will be part of a team focused on bioinformatics and computational biology support of translational and exploratory data analysis. The scientist will apply expertise in bioinformatics and computational biology to analyze various types of OMICS molecular profiling and phenotype data with special emphasis on metagenomics, metatranscriptomics, meta-metabolomics and metaproteomics to support Takeda discovery and development projects.
- Serve as computational biology lead for project support on translational and exploratory data analysis.
- Performs advanced analysis of metagenomics, metatranscriptomics, meta-metabolomics and metaproteomics and other OMICS data in combination with publicly available genomics data by applying unsupervised and supervised machine learning algorithms to extract novel and biologically meaningful information.
- Method development for translational research, e.g. predictive modeling for patient stratification and disease indication selection.
- Integrates genomics, genetics, epigenetics and literature data to strengthen understanding of diseases and treatment perturbations.
- Utilizes multiple approaches for analyses of genes, pathways and networks.
- Writes study reports and presents data effectively in all settings and with participants of all levels of the organization.
DIMENSIONS AND ASPECTS
Technical/Functional (Line) Expertise
- Demonstrates theoretical knowledge of computational biology, statistical genetics and genomics
- Keeps current with emerging trends in computational biology, statistical genetics and genomics
- Has a solid background in basic cellular and molecular biology with an understanding of a range of disease and therapeutic areas including neurobiology, gastroenterology, immunology, rare diseases and oncology.
- Fluent in the use of R, Bioconductor, Python, and/or other languages commonly used for statistical genetics and computational genomics analysis.
- Must be expert in sequencing data analysis such as RNAseq, DNAseq, scRNA-seq, etc.
- Must have a proven track record in the analysis of large OMICS data
- Is able to develop creative methods for integration of human genetic, epigenetic, gene and protein expression data
Decision-making and Autonomy
- Independently manages own workload; takes responsibility for his or her own performance and accepts full ownership of issues, problems, and opportunities, regardless of the source
- Scientifically independent; Comfort with initiating analyses without always having a clear direction planned in advance
- Conducts scientific presentations to internal and external audiences; excellent communication, interpersonal sensitivity, and negotiating skills
- Receives high level instructions on all work, determines methods on new assignments, works closely with manager, may manage junior staff. Demonstrated creativity and innovation, including ability for divergent thinking and the propensity to question to traditional methods, processes, and products, as well as build on others' ideas.
EDUCATION, BEHAVIOURAL COMPETENCIES AND SKILLS:
- PhD degree in a scientific discipline, or
- MS with 6+ years experience, or
- BS with 8+ years experience