Applied Scientist - Pipeline Data Science

About the position

The Seeds Development department for Syngenta seeks a passionate candidate for a full-time position of: Applied Scientist - Pipeline Data Science. As our Applied Scientist - Pipeline Data Science, you will play a crucial role in designing scalable data solutions that drive actionable insights. By consolidating complex datasets into robust, efficient pipelines, you’ll empower teams to make better decisions and uncover new possibilities in plant breeding. This is your opportunity to work on impactful projects in a collaborative and cutting-edge environment where data drives transformation.

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: Develop systems to track, monitor, and maintain the quality, consistency, and integrity of data to ensure robust outputs for analysis and decision-making.
• 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.

Requirements

• Master’s degree in Computer Science, Statistics, Applied Mathematics, or a related field.
• Proficiency in Python, R, and SQL.
• Experience in both relational and NoSQL databases.
• Familiarity with AWS services and cloud infrastructure.
• Exceptional analytical skills, with the ability to identify patterns and trends in large and diverse datasets and translate them into meaningful recommendations.
• Strong team player who thrives in cross-functional settings and communicates technical concepts effectively to diverse stakeholders.

Nice-to-haves

• Hands-on experience with containerization tools (e.g., Docker, Kubernetes).
• Experience with machine learning frameworks (e.g., Keras, PyTorch, scikit-learn).
• Interest in plant breeding or agricultural innovation.

Benefits

• Competitive salary package.
• Flexible working hours.
• Excellent benefits.