At FMC, the Data Science, Engineering and Analytics team powers innovative solutions across the enterprise, with roots in our industry-leading precision agriculture initiatives. While we continue delivering novel insights that help farmers and customers protect their crops against various pest pressures, our team has expanded to develop comprehensive data solutions throughout FMC's operations. As the only global agricultural sciences company focused exclusively on crop protection, FMC leverages our data expertise to deliver best-in-class agronomic advice and optimize business processes across the organization. Our team builds and maintains a diverse portfolio of applications and services, adopting the mindset, agility, and processes of a technology company while drawing on our 100+ years of experience in agriculture. 

This role is located at our corporate headquarters in Philadelphia, PA. 

 

Responsibilities

• Build data science solutions to solve complex problems and fuel growth initiatives
• Work with stakeholders to find workable solutions to our business problems on data. This will require the ability to translate the problems stated in business terms into quantitative approaches, leading to projects starting small and aspiring to grow big if preliminary results are promising.
• Develop data pipelines (Airflow and Databricks) for automation, scalability, extensibility, and transparency
• Convey sophisticated machine learning and modeling solutions with intuitive visualizations and effective communications
• Participate in discussions to make the right scientific or technical trade-offs to meet long-term strategic and short-term tactical business needs
  
  

 

Qualifications

• MS/Ph.D. in computer science, machine learning, statistics, economics, applied mathematics, Physics, agronomy/climatology, or other scientific or computational disciplines
• 4+ years of industry experience building advanced analytics models and machine learning products across one or more areas of Predictive/Prescriptive Analytics, Natural Language Processing, Time Series Forecasting, Computer Vision, and Image Processing
•  Applied experience of various machine learning techniques (e.g., Neural Networks, Tree based learning, Clustering etc.)
•  Hands-on experience working on large data sets with expertise in data mining, predictive modeling, statistical analysis
• Experience with distributed systems such as Hadoop/MapReduce, Spark, etc. and modeling on cloud platforms (AWS/Azure/GCP)
• Fluency with at least one of the modern distributed ML frameworks such as TensorFlow, PyTorch, Keras, H2O etc.
• Demonstrated expertise in SQL and one or more languages commonly used in data analytics (Python, R, Scala etc.). Python/R/Scala proficiency is strongly preferred
• Experience with packages such as NumPy, SciPy, pandas, scikit-learn, dplyr etc.
•   Passionate about data driven story telling using visualization tools like Tableau, Power BI.
•  Demonstrated experience building and deploying production ready machine learning models
•  Comfortable with multi-tasking in a fast paced and fun environment 
  
  

 

Preferred Qualifications

• Hands on experience working with large data sets (Spark, Hadoop, Hive, Redshift, etc.)
• Experience articulating business questions and using mathematical techniques to arrive at an answer using available data
• Additional experience in one or more core analytic methods, such as predictive modeling customer segmentation/clustering, voice of customer applications, personalization, image recognition, supply chain optimization etc.
• Good understanding of data privacy, GDPR and best practices around data encryption
• Working knowledge of DevOps model, automated workflows, associated CI/CD components and containers (git, Jenkins, Docker, Kubernetes etc.)
• Well-versed in articulating business questions and using mathematical techniques to arrive at an answer using available data
• Project management experience managing vendors and system integrators to lead data engagements and drive adoption
• Experience analyzing geospatial information and applying geostatistical techniques to remote sensing data