How data science is shaping environmental research

Large companies all over the world make use of large amounts of data and machine learning to benefit their work, so why shouldn’t the important work being done to study the planet make use of such tools as well? Environmental Science has increasingly incorporated data science into its toolset over the past several years, to gain greater understanding of changes to our planet’s climate and understanding of weather patterns, even going as far as predicting extreme weather events.

Below are a few examples of what this can look like in a real-world context, along with an example of an environmental data scientist position.  

Increasing prevalence 

Environmental science is not always the first thing that comes to mind when thinking about data science. Over the past decade, its use in the field has become more common. 

This growing movement is reflected in the emergence of new professional communities, dedicated academic journals, and specialized degree programs over the past decade.

One such group is Climate Informatics, a professional group that is enthusiastic about combining statistics, machine learning, and data mining. Through online community building and an annual conference, they bring together members of the field to cultivate new ideas and collaboration. 

As for journals, Environmental Data Science is a relatively new journal from Cambridge University Press that started publishing in 2022 and has continued putting out articles through at least 2025. It shows up in standard academic indexes and archives, and most of the papers sit at the overlap of machine learning, statistics, and environmental research. 

Lastly, there are a wide variety of post-secondary programs available that focus on the use of data science in environmental science. Some prominent examples are through the University of Michigan, Stanford, and the University of Chicago.  

Over the past decade, there has been a growing number of avenues for exploring this subfield and learning more about the exciting and innovative results of this interdisciplinary research.  

Where does the data come from? 

Data scientists working in this space for the environment are typically not working with a single dataset. Instead, analysts combine massive collections of satellite observations, ground-based sensors, long-term climate records, and other monitoring systems. 

For example, NASA’s Earthdata portal hosts thousands of open climate and environmental datasets, while NOAA’s observing networks deliver high-resolution weather and ocean data that feed forecasting and modeling systems. Datasets like the Global Historical Climatology Network provide decades-long records that are essential for trend analysis and machine learning applications.  

As in many fields, the volume of data is growing, the amount of data is growing year to year, leading to increased capabilities and potential applications.  

Innovative Uses 

Wildfire Simulations 

One example of such applications is the innovative approach to simulating the impact that prescribed wildfires will have on air quality (intentionally set, controlled fires used to reduce fuel buildup and limit the severity of future wildfires). This technique marks a notable shift from simply examining what has happened in the environment to what could happen. 

By predicting the air‑quality impacts of prescribed burns, these models enable agencies to plan proactively and determine optimal burn timing. Simulations are run to see how pollution levels change based on controlled fires during different times of the year, which lead to actionable insights. Data scientists found out that burns conducted during wildfire seasons compound pollution impacts, leading to higher levels of harm.  

Furthermore, by examining short-term and long-term impacts through a data-informed lens, scientists were able to determine that despite the short-term harm of prescribed burns, it improves air quality by keeping wildfires in check. This type of insight is only possible with responsive, data-driven simulations that model future possibilities at a large scale.  

Flood Prediction  

Alongside simulating wildfires, large datasets can also be leveraged to predict flooding. This can be done even without real-time data from bodies of water. Instead, it combines meteorological forecasts with previously displayed patterns to achieve a similar effect. 

By using historical data, and not just relying on real-time sensors, the impact of this technology can be scaled beyond data rich regions and into environments where infrastructure is not as prevalent. The data models learn the historical patterns of flooding occur, and use that to inform communities of potential risks.  

Example Job Posting

To get a clearer picture of what data science can look like in environmental work, here’s an example of a role focused on climate and geospatial analytics at a company working with satellite and environmental data. (Some of the original wording has been adjusted for clarity and formatting.) 

• Develops and deploys machine learning models that detect and predict environmental changes such as deforestation, wildfire spread, and land use shifts over time. 
•  Applies techniques from computer vision, geospatial analysis, and time-series modeling to large-scale satellite imagery and climate datasets, generating insights that support environmental monitoring and response efforts. 
   
•  Collaborates with climate scientists, engineers, and policy teams to design analyses that evaluate environmental risks and assess the impact of potential interventions. 
   
•  Builds and maintains data pipelines that integrate remote sensing data, weather patterns, and ground-based observations, ensuring systems are scalable and reliable for continuous monitoring. 
   
•  Translates complex model outputs into clear, actionable insights for stakeholders, including governments, nonprofits, and organizations focused on sustainability and resource management. 
   
•  Contributes to the development of tools and platforms that allow non-technical users to explore environmental data and make informed decisions. 

This role shows how data science operates within environmental and climate-focused work. It combines technical modeling with real-world application, requiring both strong analytical skills and the ability to communicate findings across scientific, policy, and operational contexts. It’s a strong example of how data scientists contribute to understanding environmental change, managing natural resources, and supporting decisions that affect communities and ecosystems over time.

Conclusion 

Data is becoming central to how we understand and respond to environmental challenges. From predicting air quality and flood risk to monitoring land use and climate trends, data science is shaping how decisions are made across science, policy, and industry. 

If you’re thinking about a career at the intersection of data science and environmental work, consider talking to someone in the field to learn more about how they got started and what their day-to-day work looks like. If you’re curious about how your background might apply or what next steps to take, consider connecting with CAPD. We’re here to help you explore your options.