We are looking for a highly motivated and skilled Computational Biology Data Scientist to join our growing Crop Breeding, Data Science, Science, and Digital teams, and contribute to the characterization of both existing and novel genetic diversity generated through our genome editing capabilities to its strategic deployment in our crop product development pipeline. We are interested in candidates with a background in Bioinformatics, Computational Biology, Genetics and Genomics, to help support our ongoing efforts towards germplasm characterization and generation of genetic diversity to drive genetic gain in plant breeding. The role will be based at Inari’s headquarters located in Cambridge, MA.
AS THE COMPUTATIONAL BIOLOGY DATA SCIENTIST, YOU WILL…
- Engage and collaborate with a team of Scientists from multiple biological disciplines, Plant Breeders, Bioinformaticians, Computational Biologists, and Software Engineers to integrate environmental, phenotypic, and molecular datasets, and build data-driven solutions supporting our genome edited-product development pipeline across crops.
- Develop computational models to integrate environmental, phenomics, genomics, transcriptomics, proteomics and potentially other types of biological (‘omics’) information to build a predictive platform for complex traits.
- Implement machine learning and/or other non-linear or linear algorithms to enable the performance prediction of novel genetic diversity derived from our genome editing technologies, using a combination of environmental and multiple ‘omics’ datasets.
- Work with Products, Science, and Digital teams to develop, prototype, and implement data pipeline and front-end applications that increase the efficiency of genome edited breeding programs across crops, by deploying predictive models.
- Contribute to our ongoing germplasm characterization efforts, including both naturally occurring as well as novel genetic diversity derived from our genome-editing technologies.
- Contribute to the development of highly accurate training sets for genomic selection of complex traits, including the design, data generation, and analysis of field and molecular experiments.
- Develop, improve, or expand in-house computational pipelines, algorithms, models, and services used in crop product development.
- Constantly document and communicate results of research on data mining, analysis, and modeling approaches to ensure strategic forward planning and alignment within and across teams.
YOU BRING …
- Ph.D. in Computational Biology, Data Science, Computer Science, Bioinformatics, Plant Biology, Quantitative Genetics, Genetics and Genomics, or other relevant scientific fields.
- Expertise with tools for data analysis, statistical computation, and visualization (such as Python, R); programing and pipeline development skills integrating large, complex datasets from distinctive structured and unstructured sources.
- Demonstrated skills to use simple interfaces to build visualization capabilities of analysis outputs and results.
- Demonstrated experience developing and executing machine learning, deep learning, and data mining algorithms, statistical methodologies, and/or other predictive and simulation models.
- Experience with complex, multi-source, multi-year, and multi-location biological datasets typical of agricultural systems.
- Experience working with various types of molecular and genetic data, including DNA, RNA, metabolites, proteins.
- Familiarity with molecular marker-based studies applied to plant breeding, such as marker-assisted selection, genome-wide phenotype-to-genotype association, and genomic selection.
- Outstanding problem-solving skills and proven ability to develop, deliver, and deploy analytical solutions by: scoping the problem and defining hypotheses, identifying and using necessary data sources, building pipelines for data integration, analysis, modeling, and simulation, validating models, and prototyping actionable outcomes.
- Ability to align and integrate data science research into crop product development pipelines and materials.
- Ability to efficiently summarize and effectively communicate results to a wide variety of audiences; strong communication skills.
- Ability to work in a fast-paced, cross-functional, collaborative environment, completing projects on time.
- Creative, innovative, and strategic thinking; willingness to be bold and take risks on novel ideas.
- Curiosity and a desire to continuously learn and have a meaningful impact.
- A collaborative approach, open to giving and receiving ideas, perspectives, and feedback.
- Background in agronomy, agriculture, and/or plant or animal breeding.
- Experience working with any of the following: AWS, Docker, Git, Jupyter, APIs.
- Experience in genomic selection applied to plant or animal breeding.