About Inari...

Inari is the SEEDesign™ company. We embrace the diversity and complexity of nature in every aspect of our business to drive innovation – to push the boundaries of what is possible. Through our unrivaled technology platform, Inari uses predictive design and advanced multiplex gene editing to develop step-change products. We are taking a nature positive approach to unlock the full potential of seed that will transform the food system. The results will lead to more productive acres delivering value creation for farmers and a more sustainable future for our planet.

Our success is dependent on great minds, collaborating to generate bright ideas and deliver exceptional outcomes.

We have over 230 employees, with research sites in Cambridge, MA (USA) and Ghent (Belgium), as well as a product development site in West Lafayette, IN (USA). We’ve deliberately built a team that brings diversity of thought to all aspects of our business, to generate new ideas, approaches, and ways of operating. And we've intentionally combined experience with potential, bringing agriculture industry experts with the desire to innovate together with bright minds from academia, human therapeutics, software, and consulting. If you want to be part of a diverse and inclusive team developing unique solutions to feed the world while protecting our planet’s natural resources, we’d love to hear from you!

We are looking for a Computational Biologist with a background in Quantitative Genetics to join our growing team and help us expand our biological data analysis and computational capabilities to better prioritize targets for genome editing in crops. The role will be based in Cambridge, MA.

As a Computational Biologist you will: 

  • Develop and adapt quantitative genetic approaches to link genotypes to phenotypes and prioritize genetic variants for intervention in major crop species 
  • Apply methods from quantitative and population genetics to analyze genetic variant data derived from whole-genome sequencing and genotyping 
  • Creatively design experiments and leverage public and in house endophenotypic data (RNAseq, metabolomics, proteomics, etc) to choose candidates to improve complex, polygenic traits
  • Work together with a team of computational scientists, plant geneticists, and software engineers to efficiently meet the project’s needs
  • Present results to internal and external scientists through talks, peer-reviewed manuscripts, and conference presentations

You bring : 

  • Ph.D. or M.S. with 5 or more years of relevant experience in Quantitative Genetics, Population Genetics, Computational Biology, Statistics, Genetics, Plant Breeding, Animal Breeding, or other relevant scientific field 
  • Extensive experience working with genetic variant data and large-scale omics datasets
  • Advanced knowledge of current literature regarding population and/or quantitative genetics, and their application in clinical genetics or agricultural species improvement
  • A comprehensive understanding of linear mixed models with demonstrated applications to marker-trait and gene-trait association (GWAS, PheWAS, TWAS, QTL mapping, etc) and/or polygenic risk score prediction / genomic prediction
  • Interest and aptitude to apply modern statistical and/or machine learning techniques to genetic variant and endophenotypic data
  • A commitment to writing and maintaining reproducible analyses and code and a strong willingness to adapt to an evolving data science tech stack
  • Experience using novel methods to combine multiple sources of genomic data to derive actionable insights
  • Conceptual understanding of wet-lab techniques including, but not limited to: molecular cloning, NGS library construction, genetic manipulation via genome editing and/or other methods
  • Advanced competency with one or more of the following {Python, R, Julia} and Linux/UNIX computing environment
  • Ability to rapidly summarize data and clearly communicate results to various audiences
  • Ability to work in a team in a fast-paced, cross-functional environment
  • Ability to manage ambiguity and to provide and receive constructive feedback
  • Adaptability and enthusiasm for new challenges, innate curiosity, and a passion for learning
  • Willingness to understand new genetic systems
  • Creative and strategic thinking, good problem solving skills, willingness to be bold and take risks, and the ability to recognize and learn from failures
  • Excellent time management and organizational skills to ensure efficiency in planning and execution

Bonus qualifications…

  • Experience working with any of the following: AWS, Google Cloud, Docker, Git, Agile methodologies
  • Familiarity with concepts associated with evolutionary genetics
  • Previous experience working with plant genomic data is a plus, but not required.
  • Familiarity with plant breeding approaches

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