ABOUT THE ROLE…
We are looking for a highly motivated and skilled Data Scientist in the Molecular Breeding – Quantitative Genetics fields to join our growing Crop Breeding, Computational Biology, Data Science, and Software Engineering teams, and help us improve and expand our digitally-enabled crop research and development capabilities applied to product development. We are interested in candidates with a background in Quantitative Genetics and demonstrated experience in predictive models for Genomic Selection, to help support our computationally driven crop breeding efforts. The role will be based at Inari’s headquarters located in Cambridge, MA.
AS THE MOLECULAR BREEDING DATA SCIENTIST, YOU WILL…
• Engage and collaborate with a team of Data Scientists, Plant Breeders, Bioinformaticians, Computational Biologists, and Software Engineers to integrate environmental, phenotypic, and genetic datasets, and build data-driven solutions for product development across crops.
• Identify and leverage existing datasets, as well as design appropriate experiments to generate high-quality datasets for genotype-to-phenotype association and genetic signal discovery studies applied to plant breeding programs.
• Work with Science and Engineering teams to develop, prototype, and implement data pipeline and front-end applications that accelerate and increase the efficiency of crop breeding programs.
• Leverage genetic and pedigree datasets to enhance germplasm characterization within breeding programs for multiple crops.
• Apply machine learning, predictive, statistical, or mechanistic models, and/or other computational approaches to unveil insights from complex, multi-year, multi-location datasets derived from agronomic eco-systems.
• Contribute to the development of training sets and deployment of genomic selection for product development across crops.
• 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 Quantitative Genetics, Computational Biology, Data Science, Computer Science, Plant Biology, Plant Breeding and Genetics, 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 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.
• Demonstrated experience working 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.
• Demonstrated understanding of plant genetics and genomics.
• 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.
• 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.
• Strong communication skills.
• Curiosity and a desire to continuously learn and have a meaningful impact.
• A collaborative approach, open to giving and receiving ideas, perspectives, and feedback.
• Experience working with any of the following: AWS, Docker, Git, APIs.
• Background in agronomy, agriculture, and/or plant or animal breeding.
• Expertise in genomic selection applied to plant breeding for product development.