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Remote Ocean Modeling Jobs in Missouri (NOW HIRING)

Remote Ocean Modeling information

What are the key skills and qualifications needed to thrive as a Remote Ocean Modeler, and why are they important?

To thrive as a Remote Ocean Modeler, you need a solid background in oceanography, mathematics, and computational modeling, often supported by an advanced degree in a related field. Familiarity with programming languages (such as Python, MATLAB, or Fortran), high-performance computing, and specialized modeling software like ROMS or HYCOM is typically required. Strong analytical thinking, problem-solving abilities, and clear communication skills distinguish top performers in this role. These competencies are vital for accurately simulating ocean dynamics, interpreting complex data, and collaborating with multidisciplinary teams in remote settings.

What is remote ocean modeling?

Remote ocean modeling is the practice of using computer simulations and mathematical models to study and predict ocean behaviors—such as currents, temperature, and tides—from a location that is not physically at sea. Professionals in this field analyze data remotely, often leveraging satellite observations, remote sensors, and advanced software to create accurate models of oceanic processes. These models are crucial for understanding climate change, supporting marine navigation, managing fisheries, and forecasting weather events. Remote ocean modeling enables researchers and organizations to monitor and assess the health of the oceans without the need for constant on-site measurements.

What are some common challenges faced by remote ocean modelers, and how can they be addressed?

Remote ocean modelers often encounter challenges such as coordinating with multidisciplinary teams across different time zones, managing large datasets, and ensuring reliable computational resources from offsite locations. To address these, it's important to establish clear communication protocols, utilize collaborative platforms for code and data sharing, and have access to secure, high-performance computing environments. Proactively scheduling regular virtual meetings and leveraging cloud-based modeling tools can also help maintain workflow efficiency and team cohesion.

What is the difference between Remote Ocean Modeling vs Remote Marine Data Analyst?

AspectRemote Ocean ModelingRemote Marine Data Analyst
Required CredentialsDegree in Oceanography, Marine Science, or related fields; experience with modeling softwareDegree in Marine Science, Data Science, or related; proficiency in data analysis tools
Work EnvironmentResearch institutions, environmental agencies, or private companies; focus on modeling simulationsData-focused roles in research, consulting, or government agencies; emphasis on data interpretation
Industry UsageUsed for climate prediction, ocean circulation studies, and environmental impact assessmentsApplied in marine resource management, policy advising, and environmental monitoring

Remote Ocean Modeling involves creating simulations of ocean processes using specialized software, primarily for research and environmental applications. In contrast, Remote Marine Data Analysts focus on analyzing marine data sets to derive insights and support decision-making. Both roles require strong analytical skills and relevant scientific backgrounds but differ in their core activities and software tools used.

What are popular job titles related to Remote Ocean Modeling jobs in Missouri? For Remote Ocean Modeling jobs in Missouri, the most frequently searched job titles are:
What cities in Missouri are hiring for Remote Ocean Modeling jobs? Cities in Missouri with the most Remote Ocean Modeling job openings:
Infographic showing various Remote Ocean Modeling job openings in Missouri as of July 2026, with employment types broken down into 87% Full Time, 10% Part Time, 1% Temporary, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.
Data Scientist Geospatial Analytics

Data Scientist Geospatial Analytics

Bunge

Chesterfield, MO • On-site, Remote

Other

Medical, Retirement, PTO

This job post has expired today. Applications are no longer accepted.


Bunge rating

7.2

Company rating: 7.2 out of 10

Based on 31 frontline employees who took The Breakroom Quiz

185th of 401 rated food and drinks producers


Job description


City : Chesterfield State : Missouri (US-MO) Country : United States (US) Requisition Number : 46371

A Day In The Life:
Leveraging our inherent market intelligence is a critical component to Bunge's success, particularly in the dynamic agricultural markets.  This the reason why Bunge has one of the large economic analysis teams in the industry. Our analysis team is comprised of over 50 analysts worldwide who gather, analyze, supply and demand and other pertinent information. The global analysts work closely with global traders to help market develop market theses that drive the company's trading and risk decisions. The team covers global grains, oilseeds, biofuels, ocean freight and livestock. 


The Data Scientist, Geospatial Analytics will be a core contributor to Bunge's Global Economic Analysis team, applying data science, satellite imagery, advanced statistical modeling, and emerging generative AI techniques to generate valuable insights across global agricultural markets. This role will lead the development of scalable, satellite-based analytics that support global crop forecasting, supply chain intelligence, and commodity market analysis. The position focuses on integrating public Earth observation data with proprietary datasets and transforming them into actionable signals that enhance our understanding of crop conditions, acreage, yield potential, and global supply-demand dynamics.   

What You'll Be Doing:
   Design, build and scale satellite-based analytics pipelines for real-time crop monitoring at globe scale;
   Analyze and integrate multi source datasets, including satellite imagery (Sentinel 1/2, Landsat, MODIS), weather and soil data, agricultural statistics, field level observations, and proprietary datasets
   Develop geospatial indicators such as NDVI anomalies, crop classifications, and yield signals to support trading and commercial decisions
   Leverage cloud infrastructure (e.g., Google Cloud Platform, AWS) for large scale geospatial data processing
   Utilize platforms and tools including Google Earth Engine, BigQuery, and Python based analytics pipelines
   Apply AI and machine learning techniques to imagery and time series data (e.g., classification, segmentation, feature extraction, and temporal modeling)
   Collaborate with economists, market analysts, data engineers, and business leaders to integrate geospatial insights into market views and fundamental analysis
   Monitor, evaluate, and continuously refine deployed models and analytics to ensure sustained accuracy and measurable business impact
   Clearly communicate complex analytical findings, model insights, and strategic recommendations to diverse audiences, including senior leadership and traders, to support informed decision making and global risk management

Skills/Experience Requirements:
   Master's degree or higher in Remote Sensing, Statistics, Computer Science, or a closely related quantitative field 
   Minimum of 5 years of professional experience in remote sensing, geospatial analytics, or agricultural data science 
   Advanced proficiency in Python (e.g., pandas, NumPy, scikit learn, statsmodels, TensorFlow, GeoPandas, rasterio) 
   Strong SQL skills for data extraction, manipulation, and analysis across large datasets 
   Solid understanding of geospatial data systems, projections, and large scale processing workflows 
   Demonstrated ability to translate complex data and models into practical agricultural, commercial, or market insights 
   Excellent communication and presentation skills, with the ability to explain complex analytical concepts clearly and concisely 
   Highly detail oriented, proactive, and self motivated, with the ability to work both independently and collaboratively in a fast paced, global environment


Preferred Skills/Experience:
   Background in agriculture, crop modeling, or commodity research 
   Experience working with radar data and vegetation indices 
   Exposure to yield modeling, acreage estimation, or crop classification workflows 
   Knowledge of agricultural commodity markets (e.g., grains, oilseeds, biofuels) and agronomic concepts


Bunge offers a variety of benefits including health and wellness plans, retirement contribution and paid vacation/holidays.


At Bunge (NYSE: BG), our purpose is to connect farmers to consumers to deliver essential food, feed and fuel to the world. As a premier agribusiness solutions provider, our team of ~34,000 dedicated employees partner with farmers across the globe to move agricultural commodities from where they're grown to where they're needed-in faster, smarter, and more efficient ways. We are a world leader in grain origination, storage, distribution, oilseed processing and refining, offering a broad portfolio of plant-based oils, fats, and proteins. We work alongside our customers at both ends of the value chain to deliver quality products and develop tailored, innovative solutions that address evolving consumer needs. With 200+ years of experience and presence in over 50 countries, we are committed to strengthening global food security, advancing sustainability, and helping communities prosper where we operate. Bunge has its registered office in Geneva, Switzerland and its corporate headquarters in St. Louis, Missouri. Learn more at Bunge.com.

Every day our people exemplify these values, which represent Bunge at its core:


   We Are One Team - Collaborative, Respectful, Inclusive
   We Lead The Way - Agile, Empowered, Innovative
   We Do What's Right - Safety, Sustainability, With Integrity

If this sounds like you, join us!  We value and invest in people who believe in our purpose and are excited to live it every day - people who are #ProudtoBeBunge



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