Data Scientist

Join Trase at the Stockholm Environment Institute and help shape data-driven insights for sustainable supply chains as a data scientist, driving positive environmental impacts across global commodities.

The role

Trase is looking for a data scientist to help develop and maintain up-to-date data on supply chains and environmental impacts across Trase’s priority commodities (with a focus on soy, beef, palm oil, cocoa and wood pulp). You will play a key role in updating supply chain and impact data on these commodities, developing and maintaining reproducible workflows and quality assurance protocols, and innovating methods and data products to link impacts to production and trade. In addition, you will contribute to actionable insights from our data to drive impact with target audiences.

This role requires a strong data scientist with experience working with both spatial and non-spatial data, and an eagerness to learn about sustainability issues and methods. The ability to collaborate with colleagues across the team is key to success in this role.

The position is part of Trase’s product team, working alongside data engineers, back- and front-end web developers, a UX designer and other data scientists. You will be an active part of a global team of multinational, multidisciplinary and multilingual experts.

Key responsibilities:

• applying existing methods to update commodity supply chains and spatial metrics on the environmental impacts of commodity production and supporting the preparation of documentation (approx. 50%)
• developing new methods, reproducible workflows, and quality assurance protocols to improve the efficiency of Trase workflows in collaboration with colleagues (approx. 20%)
• supporting the development of open-access data products, analysing Trase and third-party data to deliver key insights and summary statistics on the sustainability of commodity trade (approx. 20%)
• thinking creatively about bringing new data, technical solutions and procedures to enhance Trase’s core data offering, facilitating access and analysis of this data (approx. 10%)

Who you are

You have a strong background in data science and a deep interest in sustainability, particularly in the agricultural, land-use or forestry sectors. You are motivated by making up-to-date data and knowledge accessible to diverse audiences, including the private sector, governments and civil society.

You are able and willing to work both independently and as part of a team in a highly flexible and dynamic organization. You are comfortable delivering impactful data products and analyses, while also dedicating time to basic maintenance, monitoring, documentation and processing tasks. Respect for colleagues and a commitment to fostering a supportive, fun, and collaborative working environment are values you hold strongly.

Essential qualifications:

• master’s-level qualification or equivalent work experience in a field relevant to data science or analysis (e.g. data science, environmental sciences, economics)
• experience in data pre-processing and analysis (spatial and non-spatial)
  proficiency in one of the following programming languages: R, Python
  experience using Google Earth Engine
• experience working with Git (GitHub, GitLab or similar)

Desirable qualifications:

• experience working on the sustainability of commodity supply chains and analysis of trade data, especially in soy, beef, palm oil, wood pulp or coffee sectors
• experience in SQL and working with databases and/or similar data catalogs
• experience working in multidisciplinary teams
• experience with machine learning/deep learning algorithms and applications (e.g. ML/AI)
• proficiency in one or more of Trase’s target languages: Spanish, Portuguese, Bahasa Indonesia, French, Mandarin Chinese

How to apply

You do not need to be a Swedish or EU citizen, nor have a work/residence permit in order to apply for a job at SEI HQ. SEI can provide support for work permit sponsorship if required. Please submit your application by 17 November 2024, 23:59 Stockholm local time.