Breeding Data Scientist (m/f/d) (80-100%)
Location: Zürich, Switzerland
Employment: Contract for 3 years with the option to convert to permanent employment based on excellent performance.
About the Swiss Plant Breeding Center (SPBC)
The Swiss Plant Breeding Center (SPBC) is an innovative and independent center dedicated to enhancing plant breeding in Switzerland, supported by the Federal Office for Agriculture (FOAG). Our mission is to strengthen Swiss plant breeding and improve breeding efficiency. We focus on networking industry players and translating research results into practical breeding applications. SPBC offers technical support, expertise, and actively engages in planning and implementing innovations, accommodating all crop types, cultivation systems, and methodologies.
Role Overview
We are seeking a highly skilled and versatile Breeding Data Scientist to join our team. In this pivotal role, you will serve as the vital link between complex data and the practical realities of breeding plots in collaboration with private and public breeding organizations across Switzerland. Your mission is to utilize genomics and phenotype data to expedite the development of resilient, high-quality crop varieties tailored to the Swiss environment. This is a unique opportunity to play a significant role in shaping the future of Swiss agriculture and the food system.
Key Responsibilities
- Data analytics and biostatistics across various crops to enhance breeding success and increase genetic gain.
- Pipeline Development: Build and maintain automated bioinformatics pipelines for processing breeding data, including high-throughput sequencing (GBS, WGS, RNA-Seq) and digital phenotyping (images).
- GWAS & QTL Mapping: Conduct Genome-Wide Association Studies and QTL mapping to identify genetic markers linked to critical traits such as disease resistance, product quality, and environmental adaptation.
- Molecular Marker Development: Design cost-effective markers and validation experiments in collaboration with breeders for routine marker-assisted selection.
- Predictive Models and AI Applications: Develop and implement genomic and phenomic selection workflows with AI integration.
- Data Integration: Harmonize diverse datasets, including phenotypic field data, environmental variables, pedigree, and genotypic data to optimize breeding outcomes.
- Scientific Computing Environment: Architect and maintain the SPBC scientific computing infrastructure and IT environment.
- Stakeholder Engagement and Training: Advise Swiss plant breeders on the usage of new technologies and methods, fostering collaboration within the Swiss breeding sector and training team members.
- Decision Support: Translate complex statistical outputs into clear visualizations and actionable recommendations for breeding teams to inform selection decisions.
Who You Are
- You thrive in a dynamic work environment and enjoy tackling diverse and practical scientific challenges across various plant species within a complex breeding network.
- You possess effective communication skills and can engage with a wide range of stakeholders.
- You are driven by applying new technologies and methodologies in practical breeding, understanding that publishing is not the primary focus of this role.
- You enjoy developing efficient processes and workflows for routine application.
- You have a solution-oriented mindset and are enthusiastic about sharing your expertise.
- You like working in a small, collaborative team where support is mutual.
Technical Requirements
- Education: PhD (or MSc with >6 years of experience) in Plant Breeding, Quantitative Genetics, Bioinformatics, Data Science, or a related field.
- Programming: Advanced proficiency in R and/or Python.
- Genomics: Experience in generating sequencing libraries, genome assembly, alignment, variant calling, and imputation, along with database establishment.
- Digital Phenotyping: Knowledge of processing image and/or sensor data.
- Quantitative Genetics: Strong understanding and experience with quantitative genetics methodology and the breeder’s equation.
- Statistics: Proficiency in linear mixed models, experimental design, machine learning, and AI applications.
- Cloud/HPC: Familiarity with high-performance computing (HPC), including Linux environments and cloud computing (AWS/GCP).
- Server Maintenance: Experience with the setup and maintenance of server infrastructure.
- Languages: Proficiency in English and either German or French.
Application Process
Apply online using the form below. Only applications matching the job profile will be considered.