Quantitative Genetics Scientist (m/f/d) for Sugarbeet and Cereals at KWS Group
Einbeck, , Germany -
Full Time


Start Date

Immediate

Expiry Date

20 Nov, 25

Salary

0.0

Posted On

21 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

We are looking for a Quantitative Genetics Scientist (m/f/d) for Sugarbeet and Cereals for a permanent employment contract, on a full-time basis within KWS SAAT SE & Co. KGaA at our headquarters in Einbeck (Lower Saxony / Germany).
In this position, you will work closely with the global sugar beet and cereals breeding and breeding technology application teams. You will have the opportunity to contribute to the development and application of innovative predictive breeding tools.

Responsibilities

YOUR RESPONSIBILITIES:

  • Ensure and optimize application support in data analytics and biostatistics across multiple crops to drive breeding program success and increase rate of genetic gain.
  • Develop a deep understanding of crop-specific data analysis needs based on breeding strategy and operations
  • Critically evaluate and continually expand quantitative genetics capabilities to improve breeding programs.
  • Maintain a strong understanding of scientific developments related to data analysis methods.

YOUR TASKS:

  • Works independently to perform standard quantitative genetic analyses, including using mixed linear models to analyze phenotypic data, performing genomic predictions, analyses for cross planning, and monitoring genetic gain and genetic diversity.
  • Proactively develops, optimizes and automates data analysis pipelines and systems in collaboration with other biostatisticians and members of the Breeding Information and Digital Product Management teams.
  • Works closely with the Biostatistics and Digital Product Development teams to identify, test and implement innovative methods related to breeding program optimization, data-driven decision making, statistical analysis, simulation, algorithmic optimization.
  • Regularly reviews emerging literature related to the implementation and validation of data analytics.
  • Participates in collaborative scientific projects with public institutions
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