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Carbon Modeler
2 months ago
Olam Agri ~ A differentiated, and market leading global agri-business focused on high-growth end consumption markets.
Our food, feed, fibre, agri-industrials & ag-services capabilities span 30+ countries, and we participate in global food and agri-trade flows, working with farmers and customers to connect local origins and global destinations. With a global origination footprint and processing capabilities, we transform food, feed and fibre to create value for customers and enable farming communities to prosper sustainably, to meet the changing consumption, food security and sustainability needs shaping the global food and agricultural landscape.
Role Overview
- Apply models to quantify carbon avoided or removed in the context of wide-ranging emission reduction projects within the own estates and supply chains
- Use data analytics as required by carbon methodologies to calibrate and validate mechanistic and empirical models, estimate uncertainty in C estimations etc.
- Collaborate with the climate action manager in designing projects to acquire accurate data for projects covering e.g. climate, water, soils, energy use, full life cycle of agri crops in a broad range of products and production systems as well as geographic origins.
- Support other members of Environmental team with research and analysis of farm-level and landscape-level data (ex. yield, carbon, biodiversity, water) in key production origins
- Identify inconsistencies with data and resolve issues, documenting the learning process.
- Support the development of visual materials that illustrate findings and results from large amounts of data in the form of presentations and reports.
- Staying abreast of trends and best practices in data mining and analytics, impact metrics and GIS, especially with applications for sustainable agriculture, monitoring land use change, and assessing social impact repetition.
Qualifications
- Master's or equivalent experience in data analytics, statistics, environmental anaytics, data science or equivalent quantitative research experience if she/he has a different academic background
- A personal passion for sustainable agriculture and food systems
- Demonstrated ability to work with large volumes of data
- Excellent analytical skills and advanced competency in Microsoft Excel, R, Python, etc.
- An understanding of different carbon and other environmental standards especially related to land use sector.
- Ability to work closely with other data analysts, collaborating on data needs and acquisition
- Understanding of Machine Learning frameworks, tools, and algorithms are desirable
- Strong leadership and interpersonal skills, with a proactive, solution-oriented and innovative mindset
- Critical thinking skills, organizational skills, and the ability to design and manage programmes.
- On-the-ground experience and a good understanding of agri-business models and success factors are highly desirable
- Willing to occasionally travel to remote operational areas
- 4-6+ years of relevant professional experience