Applied Scientist, Plant Breeding Data Science

Syngenta Group; North Carolina; On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Flexible work options

Medical, Dental & Vision benefits

401k with company match

Paid Vacation & Holidays

Education assistance

Job summary

Syngenta is seeking an Applied Scientist – Pipeline Data Science to design scalable data solutions for plant breeding insights. You will consolidate complex datasets into robust pipelines, enabling faster, data-driven decisions across IT, data science, and business teams.

The role emphasizes data quality, modeling, and mining to support innovative analytics, with opportunities to apply machine learning on genomic prediction methods and cloud platforms.

Qualifications

• Master's in CS, Statistics, Applied Mathematics, Quantitative Genetics, or related field.
• Proficient in Python, R and SQL with experience in relational and NoSQL databases.
• Experience with AWS services and cloud infrastructure.
• Experience with ML frameworks (Keras, PyTorch, scikit-learn) and data pipelines.
• Experience building scalable data pipelines for data-driven plant breeding insights.

Responsibilities

• Build scalable data pipelines consolidating data from multiple sources.
• Ensure data quality and reliability across datasets and analytics.
• Drive data modeling and mining to support advanced analytics.
• Collaborate with IT, data science teams, and business stakeholders to align data solutions.
• Identify opportunities to apply ML frameworks and cloud platforms to predictive pipelines and genomic prediction.

Skills

Python

R

SQL

Analytics

Collaboration

Education

Master's degree in Computer Science or related field

Tools

Docker

Kubernetes

ML frameworks (Keras, PyTorch, scikit-learn)

AWS

Job description

Syngenta is seeking an Applied Scientist – Pipeline Data Science to design scalable data solutions for plant breeding insights. You will consolidate complex datasets into robust pipelines, enabling faster, data-driven decisions across IT, data science, and business teams.

The role emphasizes data quality, modeling, and mining to support innovative analytics, with opportunities to apply machine learning on genomic prediction methods and cloud platforms.