Sr. Data Scientist

Florida Crystals Corporation is a fully integrated cane sugar company. Florida Crystals regeneratively farms sugarcane and rice in South Florida, where it owns two sugar mills, a sugar refinery, a packaging and distribution center, Florida's only rice mill, a compost facility, and one of the largest renewable power plants of its kind in the U.S., which uses sugarcane fiber to generate eco-friendly energy that powers its sugar operations. Florida Crystals owns one of the largest Regenerative Organic Certified® farms in the U.S. and its Florida Crystals® products are the only ROCTM sugar grown and milled sugar in the country. Florida Crystals owns ASR Group International, Inc., a holding company that conducts operations through its subsidiaries. The ASR Group® family of companies make up the world's largest refiner and marketer of cane sugar. Florida Crystals is headquartered in West Palm Beach, Florida. Learn more at www.FloridaCrystalsCorp.com.OVERVIEWReporting to the VP of R&D, the Senior Data Scientist will serve as a high-level technical contributor within the R&D group, leading the design, development, validation, and implementation of advanced artificial intelligence (AI), machine learning, and data science solutions that improve sugarcane research and operational decision-making. This role is intended for a highly capable professional with graduate-level training, who can translate complex agricultural and industrial problems into scalable analytics products, predictive models, and decision-support tools. The position will focus on developing and applying AI-driven solutions across sugarcane breeding, crop nutrition, crop health, agronomy, field experimentation, harvesting, logistics, and related industrial systems. The individual will work closely with scientists, field teams, operations personnel, engineers, and external technology partners to identify opportunities, structure data assets, prototype and test models, validate outputs under real-world conditions, and support adoption of new tools that improve productivity, efficiency, and research insight. This role requires both scientific rigor and practical execution, including hands-on engagement with field and mill data, geospatial information, remote sensing platforms, sensor technologies, and modern machine learning workflows. DETAILED ROLES & RESPONSIBILITIESLead the identification, definition, and prioritization of AI, analytics, and digital opportunities that can improve sugarcane research, crop management, resource use efficiency, operational performance, and decision quality across the R&D function. Design, develop, test, and refine advanced machine learning, statistical, optimization, computer vision, time-series, and predictive models using data from field trials, laboratory analyses, farm operations, remote sensing platforms, weather systems, equipment, and business records. Build data pipelines, modeling workflows, and reproducible analytical processes that integrate multiple data sources into reliable, usable, and well-documented datasets for research and operational applications. Develop AI-enabled tools and decision-support solutions for applications such as yield prediction, variety performance analysis, crop nutrition recommendations, irrigation and stress monitoring, disease and pest detection, image-based scouting, harvest planning, logistics optimization, and mill process improvement. Apply geospatial analytics, GIS, drone imagery, satellite imagery, proximal sensing, and other digital agriculture technologies to evaluate spatial variation, monitor crop status, and generate actionable insights for research and operational teams. Establish appropriate model development standards, including experimental design, feature engineering, validation protocols, error analysis, performance benchmarking, explainability, and continuous improvement of model quality. Translate technical findings into clear recommendations, dashboards, reports, visualizations, and presentations that support scientific interpretation, operational decisions, and leadership discussions. Partner closely with operations teams and R&D scientists to understand workflows, define success metrics, validate outputs, and ensure that analytical tools solve practical business and research problems. Support data governance and data quality by establishing clear documentation for data sources, assumptions, transformations, metadata, code, models, and decision rules, ensuring analytical work can be audited, repeated, and maintained over time. Collaborate with internal and external technology providers, universities, startups, and vendors to evaluate emerging AI platforms, sensing technologies, and analytics tools, and recommend fit-for-purpose solutions for the organization. Participate in field visits, trial reviews, sampling activities, and operational observations as needed to understand data generation processes, validate model outputs, and ensure solutions are grounded in field reality and biological context. Contribute to the deployment and adoption of analytical solutions by supporting implementation planning, user training, workflow integration, model monitoring, and feedback loops that improve performance over time. Maintain awareness of advances in AI, machine learning, geospatial analytics, digital agriculture, and scientific computing, and proactively identify innovations that can strengthen the R&D portfolio and improve how work is executed. Comply with and help reinforce all Environmental Health and Safety, data stewardship, confidentiality, and company policies applicable to research, field activities, technology use, and responsible AI practices. ESSENTIAL CAPABILITIES (KNOWLEDGE, SKILLS, ABILITIES AND PERSONAL ATTRIBUTES) Advanced AI and Machine Learning Expertise – Strong knowledge of supervised and unsupervised learning, predictive modeling, deep learning, time-series analysis, optimization, anomaly detection, and model evaluation, with the ability to apply the right methods to complex agricultural and operational problems. Programming and Scientific Computing – High proficiency in Python and/or R for data analysis, model development, automation, and reproducible workflows, with the ability to work in SQL and manage large, multi-source datasets. Data Engineering and Model Operations – Experience building data pipelines, preparing analytical datasets, and supporting deployment, monitoring, documentation, and lifecycle management of AI solutions. Geospatial and Remote Sensing Analytics – Working knowledge of GIS, spatial statistics, georeferenced data, drone and satellite imagery, remote sensing indices, and spatial analysis for agricultural monitoring and site-specific decisions. Experimental and Statistical Rigor – Strong knowledge of statistics, experimental design, validation, uncertainty, and biological and operational data interpretation, with the ability to separate signal from noise and communicate practical significance and limitations. Agricultural and Applied Research Understanding – Ability to work effectively in agricultural research settings and understand field trials, crop variability, biological systems, sampling, and implementation constraints. Experience in crop science, agronomy, plant breeding, soil science, precision agriculture, or related fields is strongly preferred. Problem Solving and Innovation – Ability to frame ambiguous problems, develop practical solutions, test alternatives, and drive meaningful improvements with curiosity, initiative, and impact. Communication and Influence – Excellent written and verbal communication skills, with the ability to explain complex analytics to technical and non-technical audiences and support adoption of new tools and methods. Collaboration and Cross-Functional Engagement – Ability to work effectively across scientific, operational, and technology teams and collaborate with internal and external partners to move initiatives forward. Organization, Ownership, and Quality Focus – Highly organized and detail-oriented, with the ability to manage multiple priorities while maintaining strong standards for data quality, documentation, timeliness, and scientific integrity. Technology Stack Familiarity – Experience with tools such as Power BI, advanced Excel, SQL, GIS platforms, cloud analytics environments, and machine learning frameworks, with familiarity in MLOps, model versioning, and workflow automation preferred. Adaptability and Resilience – Comfortable working in a dynamic research and operations environment where priorities shift, data may be imperfect, and solutions must balance rigor with practicality. Ethics, Integrity, and Responsible AI – Sound judgment, discretion, and a strong commitment to ethical conduct, responsible AI use, confidentiality, and data governance. Field Readiness – Willingness and ability to work outdoors in South Florida conditions, visit research and operational sites, and engage directly with field processes to understand context and validate solutions.