The Data Scientist will be responsible for designing, developing, and maintaining data systems and analytical workflows within the Technical and Carbon Team. The role focuses on structuring, integrating, and analysing diverse social, ecological, spatial, and carbon datasets to support data-driven decision-making and robust carbon monitoring. Working closely with the Technical and Carbon Team, the Data Scientist will play a key role in ensuring data integrity, developing automated data pipelines, and applying statistical and machine learning approaches to generate insights that enhance the efficiency, monitoring, and impact of NatCarbon’s restoration programmes. This position sits at the intersection of ecological restoration, carbon monitoring, and advanced data analytics, supporting one of South Africa’s largest landscape-scale restoration initiatives. Key Job Requirements and Expectations Data Systems Development & Management Design, develop, and maintain databases and data management systems to store and manage social, ecological, spatial, and carbon datasets. Implement scalable data pipelines ensuring efficient extraction, transformation, and loading (ETL) of data from field systems, remote sensing platforms, and operational databases. Develop and maintain automated data workflows to support routine monitoring, reporting, and analysis. Implement data validation, governance, and quality control processes to ensure the accuracy and integrity of project datasets. Maintain documentation and reproducible data workflows to support transparency, collaboration, and long-term project data management. Optimise data storage and processing infrastructure to support the increasing scale of restoration monitoring activities. Data Analysis & Insights Generation Analyse ecological, carbon, and social datasets to generate insights that support restoration planning, monitoring, and carbon credit assessments. Apply statistical and machine learning techniques to model ecosystem recovery trends, carbon sequestration rates, and other restoration outcomes. Develop predictive models for ecosystem recovery trajectories, carbon sequestration rates, and biodiversity dynamics. Integrate spatial and non-spatial datasets to support landscape-scale analysis and restoration monitoring. Develop dashboards, automated reports, and data visualisations to communicate insights effectively to both technical and non-technical stakeholders. Support the development of analytical approaches that strengthen monitoring, reporting, and verification (MRV) processes for restoration and carbon projects. Collaboration & Stakeholder Engagement Work closely with the Technical and Carbon Team to align data initiatives with field operations, monitoring systems, and research objectives. Collaborate with the Data and GIS Team to integrate spatial datasets and support landscape-scale environmental analysis. Engage with external researchers, universities, and technical partners to integrate emerging data science methodologies into project workflows. Provide training and technical support to team members on data tools, analytics platforms, and data management best practices. Identify opportunities to apply emerging data science approaches, including machine learning and advanced statistical modelling, to improve restoration monitoring and impact assessment. Optimise analytical workflows and data processing pipelines to increase efficiency and scalability as restoration activities expand. Contribute to the development and testing of machine learning models supporting restoration impact assessment, ecological forecasting, and carbon monitoring. Explore opportunities to integrate remote sensing data, field monitoring data, and operational datasets into unified analytical frameworks. General: Embrace a culture of continuous learning by adapting to evolving job responsibilities, driven by NatCarbon Africa’s commitment to innovation, personal growth, and shifting operational priorities Some travel within the Eastern and Western Cape may be required, including to remote locations, with occasional weekend or holiday work to accommodate schedules. Work hours may be irregular. This outline of duties and responsibilities is not exhaustive. The employer reserves the right to assign additional duties or responsibilities at any time, provided they are reasonably aligned with the scope of the role or necessitated by operational requirements. The functions and responsibilities detailed within this job description may be subject to modification at the employer’s discretion, based on evolving operational requirements, while remaining consistent with the parameters of the employee’s position Skills: Strong analytical thinking and problem-solving abilities. Ability to translate complex datasets into clear insights that support operational decision-making. Strong attention to data quality, analytical rigour, and reproducibility. Ability to work collaboratively in a multidisciplinary technical team. Excellent communication skills, including the ability to present complex data to diverse stakeholders. Strong organisational skills and the ability to manage multiple analytical projects simultaneously. Proactive mindset with a passion for applying data science to ecological restoration and climate solutions. Qualifications: Master’s degree in Data Science, Computer Science, Statistics, Environmental Science, or a related quantitative field. Knowledge and Experience: 5–10 years’ professional experience in data science, analytics, or a related technical field. Strong programming skills in Python and/or R for data analysis, statistical modelling, and machine learning. Experience with database systems and query languages (SQL, PostgreSQL, or similar). Experience developing and maintaining data pipelines and automated analytical workflows. Proficiency in geospatial data analysis using GIS platforms (e.g., QGIS, ArcGIS, Google Earth Engine, or similar). Experience working with cloud-based data infrastructure (e.g., AWS, Microsoft Azure, or similar). Knowledge of statistical modelling and machine learning techniques for predictive analytics. Experience working with large and complex datasets. Experience with version control systems and reproducible data workflows (e.g., Git). Valid Driver’s Licence (off-road driving experience advantageous). Experience working with ecological or environmental datasets, particularly related to restoration, biodiversity monitoring, or carbon sequestration. Familiarity with remote sensing and spatial modelling techniques. Understanding of carbon credit methodologies and MRV (Measurement, Reporting, and Verification) frameworks. Experience developing interactive dashboards and reporting tools (e.g., Power BI, Tableau, or similar). Experience integrating remote sensing data with field monitoring datasets. #J-18808-Ljbffr