St. Louis, MO
The successful candidate will contribute to establishing and optimizing computational methods for discovery and validation of genetic regions of interest that contribute to phenotypic traits of interest, to be targeted for crop improvement via breeding, genome editing or biotech traits. This person will work closely with Benson Hill biological scientists, data scientists, software engineers and project leaders to help maximize the impact of Benson Hill’s CropOS platform.
- Data integration across public, inhouse and partner data sets from genetic, phenotypic and genomic domains to enable predictive modelling.
- Analysis and data mining on the integrated data sets for target discovery and validation.
- Work with software developers and system architects to contribute to the design of the CropOS platform analytics.
- Examples of tasks that the candidate would be required to contribute are listed below:
- General phenotypic data analysis (QA,QC,LEASTSQUARE,BLUP)
- General genotypic data analysis (QA,QC,Frequency tests)
- Genetic diversity analysis -Gene & Genome level
- Imputation-Linkage disequilibrium / SNP tagging
- Genomic selection
- Genetic/Physical map consolidation
- Genome-wide Hapmap Construction
- Genome-wide association studies -SNP and haplotype based
- GWA Meta Analysis
- Genetic Architecture Modelling -Statistical and Mathematical
- Structural and Functional Annotation of regions of interest
- Network analysis- transcriptome and genome
- In silico simulation study design and execution for optimization of experimental parameters.
- Communicate results in written and visual formats to other scientists as well as non-scientist, clients and company leaders
- Design and execute in silico & in planta allele characterization and optimization for candidate genes or regions of interest
- Design and execute in silico & in planta functional validation for candidate genes or regions of interest
- Develop statistically appropriate methods & protocols for field and greenhouse data collection
- Pre and post season environmental characterization
Education and Experience:
- PhD in computational biology, population genetics or quantitative genetics, applied mathematics, crop science, physics, engineering, statistics, or closely related field with experience in crop systems.
- At least 5 years hands-on experience developing and analyzing complex biological data sets with actionable outputs.
- Ability to work in collaborative team, and the ability to respond positively to critiques of work.
- Agility and ability to deliver in a highly dynamic work environment with frequently changing diverse priorities.
- Excellent communication skills, both digital and in-person.
- Experience with advanced statistical concepts, approaches, packages and libraries.
- Background and experience using at least two statistical programming languages MATLAB, SAS and/or R, Python.
- Background and experience with at least one scripting language, Python preferred.
- Background and experience using AWS or experience with other cloud architectures, and parallelized computation systems.
- Knowledge and experience in the use of machine learning approaches to building and validating predictive models
- A track record of producing deliverables and meeting tight release deadlines.
- Ability to work autonomously, identify key tasks, and find solutions to challenges.
- Excellent planner, organizer, critical thinker, and problem solver.
- Strong record of personal development and learning new technologies.
- Background and experience in leveraging Bayesian predictive modelling
- Background and experience in machine learning approaches for predictive modelling
Benson Hill Biosystems is an Equal Opportunity Employer. All candidates must be legally authorized to be employed in the U.S. Benson Hill Biosystems is not able to sponsor applicants for work visas.