Biotech AI/ML Scientist

Syngenta Seeds is one of the world’s largest developers and producers of seed for farmers, commercial growers, retailers and small seed companies. Syngenta seeds improve the quality and yields of crops. High-quality seeds ensure better and more productive crops, which is why farmers invest in them. Advanced seeds help mitigate risks such as disease and drought and allow farmers to grow food using less land, less water and fewer inputs. 

Syngenta Seeds brings farmers more vigorous, stronger, resistant plants, including innovative hybrid varieties and biotech crops that can thrive even in challenging growing conditions.  

Syngenta Seeds is headquartered in the United States. 

**U.S. CITIZENSHIP OR GREENCARD AND PH.D REQUIRED**

You will work closely with trait research scientists, breeders, bioinformaticians, and software engineers to translate scientific needs into comprehensive digital solutions that transform how we discover, develop, and deploy valuable plant traits. You will collaborate with teams working on knowledge graphs related to native traits and trait x genome interactions. You will own our comprehensive biotech data model and create the foundational data infrastructure that powers ML-driven decisions across every stage of our biotech pipeline.

Duties:

• Collaborate across disciplines with trait research teams to translate scientific requirements into actionable technical strategies.
• Own the technical implementation comprehensive biotech data model as the single source of truth for all biotech analytics.
• Develop specialized and adaptive AI/ML models for biotech pipeline with particular focus on optimizing trait pipeline outcomes.
• Design and implement knowledge graphs that integrate diverse biotech data, creating unified data foundations for decision-making.
• Build comprehensive AI/ML-driven decision models for the biotech pipeline that supports and automates critical decisions making.
• Architect and maintain data pipelines to support real-time decision-making.
• Implement workflow automation systems that  enable end-to-end trait development pipelines.
• Deploy and scale solutions on AWS using SageMaker ecosystem and Snowflake integration.

Required:

• Ph.D. in Bioinformatics, Computer Science with bioinformatics specialization, Genomics, or Data Science with biology focus.
• At least 2 years experience.
• Proven expertise in knowledge graph construction and graph-based machine learning techniques applied to biological research and trait development.
• Demonstrated expertise in data pipeline development, ensuring efficient flow from various biological data sources to ML models and knowledge graphs.

Preferred:

• Background in plant genomics, trait development, or agricultural biotechnology with understanding of gene editing and transformation processes.
• 5 years experience.
• Experience with database architecture, data modeling, and designing unified data systems.
• Proficiency in building ML models that adapt and improve continuously based on experimental outcomes.

What We Offer: 

• A culture that celebrates diversity & inclusion, promotes professional development, and strives for a work-life balance that supports the team members. Offers flexible work options to support your work and personal needs. 
• Full Benefit Package (Medical, Dental & Vision) that starts your first day.  
• 401k plan with company match, Profit Sharing & Retirement Savings Contribution. 
• Paid Vacation, Paid Holidays, Maternity and Paternity Leave, Education Assistance, Wellness Programs, Corporate Discounts, among other benefits. 

Syngenta has been ranked as a top employer by Science Journal. 

Learn more about our team and our mission here: https://www.youtube.com/watch?v=OVCN_51GbNI 

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status. 

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