Applied Genetics Scientist - Pipeline Data Science

Syngenta Group•Durham, NC

23h•Hybrid

About The Position

Syngenta is a leading developer and producer of seeds, bringing farmers stronger, more vigorous, resistant plants, including innovative hybrid varieties and biotech crops that can thrive even in challenging growing conditions. The Applied Scientist - Pipeline Data Science will play a crucial role in designing scalable data solutions that drive actionable insights. By consolidating complex datasets into robust, efficient pipelines, this role will empower teams to make better decisions and uncover new possibilities in plant breeding. This is an opportunity to work on impactful projects in a collaborative and cutting-edge environment where data drives transformation.

Requirements

Master’s in Computer Science, Statistics, Applied Mathematics, Quantitative Genetics, or related fields.
Skilled in Python, R, SQL; experienced with relational and NoSQL databases.
Familiar with AWS services and infrastructure.
Able to uncover patterns in large datasets and translate findings into actionable recommendations.
Effective team player, able to communicate technical concepts to diverse stakeholders.
Candidates must reside in and be permanently authorized to work in the United States without current or future employer sponsorship. This comprises, but is not limited to, OPT, CPT, and H-1B visa holders.

Nice To Haves

Experience with Docker/Kubernetes, machine learning frameworks (Keras, PyTorch, scikit-learn)
Interest in plant breeding or agricultural innovation.

Responsibilities

Building Scalable Data Pipelines: Design, implement, and optimize data pipelines that integrate and consolidate data from multiple sources, ensuring seamless data flow and availability.
Ensuring Data Quality and Reliability: Contribute to data mining, curation, analytics and visualization of data sets to gain deep understanding of the crop(s) and enable successful application of technology
Driving Data Modeling and Mining: Create and implement processes for advanced data modeling and mining to support innovative analytical approaches.
Collaborating Across Teams: Work closely with IT, applied data science teams, and business stakeholders to align data solutions with organizational goals, addressing unique data needs efficiently.
Identify opportunities to incorporate advanced analytics, including machine learning frameworks and cloud platforms to continuously improve predictive pipelines.
Influence adoption of genomic prediction methods, contribute towards their integration in the breeding process and the transformation of respective breeding schemes.