Green Praxis offers environmental analytics solutions to companies with large land holdings to help them manage their assets in a sustainable way while remaining economically profitable. When assessing siting for a new infrastructure project such as a solar farm we assess the existing habitat richness and species biodiversity. For asset managers with large portfolios we provide scoring across a range of environment measures to identify progress over time and where improvements can be made. The company consists of environmental scientists complemented by a technology team that helps them perform analysis at scale and uncover insights.

We are looking for an environmental Data Scientist to support our mission. In this role, you will perform data analyses and develop models to support our work. Here are some examples:

• Identify land use and map habitats using multi-source data (satellite, drone, lidar), detecting changes driven by human or natural activities.

• Analyze ecological connectivity of landscapes (corridors, fragmentation, habitat continuity) to assess spatial dynamics of biodiversity.

• Build biodiversity models based on habitat and species analysis.

• Leverage large language models (LLMs) to automate and enhance certain analyses, including data interpretation, ecological knowledge structuring, and workflow assistance.

• Optimize machine learning pipelines and MLOps practices (deployment, monitoring, performance, costs).

• Write code for data collection, cleaning, feature engineering, and storage.

In addition to this core work, you will also contribute to various engineering tasks. We have a mobile and web application running on cloud infrastructure with a set of APIs. We are a small team, so everyone contributes as needed. Our tech stack includes Python, Dart, and NodeJS running on IBM, Amazon, and Google cloud infrastructures.

What we are looking for in you:

• PhD or Master’s degree with a specialization in machine learning

• 2+ years of experience in machine learning post university applied to real-world use cases

• Strong interest in ecology and willingness to build domain knowledge in this field

• Proven experience in building data pipelines

• Proven experience in building models and applying machine learning techniques

• Solid understanding of statistics

• Solid understanding of computer science fundamentals

• Ability to work with a high degree of autonomy

• Strong communication and collaboration skills

• Ability to communicate in French and English at B2 level or higher

If this sounds like you, please get in touch!