Machine Learning Engineer

• Architect and implement end-to-end machine learning pipelines, reducing deployment time, and time and effort required to develop, test, and deploy models from development to production environment.
• Work with Data Scientists teams on the development and deployment of machine learning models.
• Automate the MLOps workflow on the Syngenta Data Science Platform.
• Collaborate with cross-functional teams to define machine learning requirements and integrate models into production environment.
• Implement scalable and automated machine learning pipelines.
• Conduct exploratory data analysis and feature engineering to prepare datasets for machine learning modeling.
• Collaborate with Data Scientists teams predictive models using algorithms.
• Collaborate with DevOps teams to integrate machine learning models into production environment.

As a world market leader in crop protection, we help farmers to counter these threats and ensure enough safe, nutritious, affordable food for all – while minimizing the use of land and other agricultural inputs.  

Syngenta Crop Protection keeps plants safe from planting to harvesting. From the moment a seed is planted through to harvest, crops need to be protected from weeds, insects and diseases as well as droughts and floods, heat and cold.  

Syngenta Crop Protection is headquartered in Switzerland. 

This position requires a Bachelor's degree in Computer Science, Information Technology, or a related field of study and three (3) years of experience in the position offered or three (3) years of experience as a Data Engineer, or a closely related occupation.

• Must also have one (1) year of experience with: Java, C++, Perl, Python, and Unix scripting; SQL, NoSQL, and RDBMS databases; Hadoop ecosystem tools including HDFS, YARN, Spark, Hive, HBase, MapReduce, MongoDB, Cassandra, Spark MLIB, and Kafka.
• Develop ELT/ETL pipelines to build enterprise Data Models, process data in data lake, and load data into OLTP and NoSQL system.
• Building large scale batch and data pipeline processing frameworks in AWS Analytics cloud platform using PySpark/Scala on Glue ETL, Redshift, EMR, Kinesis, DynamoDB, and S3.
• Performing containerization and orchestration in AWS cloud platform using ECR, ECS, EKS, Airflow, and Step Functions.
• Employing best practices around deployment and operationalizing the code using CI/CD, scalability in cloud infrastructure, and Agile development methodologies.
• Working with relational and NoSQL data stores, methods, and approaches including Star and Snowflake, and Dimensional Modelling.
• Machine learning including supervised and unsupervised learning algorithms including linear regression, support vector machines, k-means clustering, hierarchical clustering, and neural networks.
• Data wrangling and processing, and big data technologies; deep learning frameworks including TensorFlow and PyTorch; neural network architectures including CNNs, RNNs, and GANs.
• Deployment tools and practices to bring learning models to production environment; Docker and Kubernetes.
• Must pass a background check and drug test before beginning employment.
• Position based in Greensboro, NC. Option to work remotely from Greensboro or Charlotte metro areas.

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.