Global Leader in Crop Physiology & Precision Breeding

Bayer

United States

Remote

USD 150,000 - 210,000

Full time

14 days+
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Job summary

Scorpion Therapeutics is seeking a Principal Crop Physiology Scientist to lead the development of improved seed products by integrating crop physiology, genomics, and data analytics. You will define strategies to inform breeding goals and leverage model outputs to guide trait improvement, working across global teams.

The role requires a PhD with 6+ years in quantitative genetics, plant breeding, crop physiology, or data science, and experience managing large biological datasets.

Qualifications

  • PhD with 6+ years in relevant fields or MS with 10+ years.
  • Expertise in quantitative genetics, biostatistics and functional genomics.
  • Proficiency in R and Python for data analysis.
  • Proven leadership in cross-functional teams.
  • Experience managing large biological datasets and implementing breeding innovations.

Responsibilities

  • Define crop physiology strategies to inform breeding goals.
  • Leverage model outputs to guide trait improvement.
  • Foster cross-functional collaborations globally.
  • Ensure knowledge transfer across teams.
  • Identify new technology opportunities and mentor talent.

Skills

Quantitative genetics
Biostatistics
Functional genomics
R programming
Python
Leadership
Big data
Breeding techniques
Seed physiology

Education

PhD in Quantitative Genetics, Plant Breeding, Crop Physiology, or Data Science
MS with 10+ years in relevant field

Tools

Genomics data analysis tools

Job description

Scorpion Therapeutics is seeking a Principal Crop Physiology Scientist to lead the development of improved seed products by integrating crop physiology, genomics, and data analytics. You will define strategies to inform breeding goals and leverage model outputs to guide trait improvement, working across global teams.

The role requires a PhD with 6+ years in quantitative genetics, plant breeding, crop physiology, or data science, and experience managing large biological datasets.

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