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Environmental data scientist London

3 months ago


London, Greater London, United Kingdom Watershed Full time
About Watershed

Watershed is a software platform for running a world-class climate program. We measure our success in the carbon reduction achievements of our customers. We are looking for team members who love product-building, want to work hard at a mission-oriented startup, and will collaborate with us in shaping the culture of a growing team.

We have offices in San Francisco, New York and London and remote team members across the US and Europe. We hope that you'll be interested in joining us

The role

Accurately measuring sustainability impacts throughout the value-chain is the first step to reduce the human footprint on the planet. Watershed is building the leading enterprise-grade software solution for companies to measure their greenhouse gas emissions as a means to discovering pathways to reduce them. And, we are building a team of scientists to implement the end-to-end measurement and sustainability analytics platform for our customers.

We're looking for an individual to own and drive the development of the foundational data assets and estimation methods needed for measuring sustainability impacts for Watershed's new Corporate Sustainability Reporting Directive (CSRD) product in Europe. You'll work with world-class scientists, software engineers, and product designers to create the best-in-class platform to power the sustainability programs of Europe's leading companies.

As part of this work, you will:

  • Work with a team of data scientists to help create a strategy to build, maintain, and expand the database needed for sustainability impact assessment throughout the global value-chain.
  • Research and develop methods to model the impact of production processes on the environment.
  • Identify relevant raw data that may include but isn't limited to input-output tables, international trade statistics, life cycle assessment (LCA) of products, greenhouse gas emissions inventory, pollutant emissions, ecologically sensitive regions, land use management and land use change, and water and resources use.
  • Collect the data identified and transform them into a professional grade database.
  • Use our tooling to calculate, test, and deploy the database in our system.
  • Work with product and engineering teams to power customer measurements in the Watershed platform.

You might be a fit if you:

  • Have 4+ years experience as a data scientist, enthusiasm for learning environmental impact modeling a plus.
  • Have experience in a scientific programming language like Python, ideally in a production environment.
  • Are an excellent verbal and written communicator. You can communicate complex topics to technical and non-technical audiences.
  • Thrive in high-volume environments, with both systematic organization to drive efficiency and meticulous attention to detail to ensure accuracy.
  • Are willing to roll up your sleeves to work with unstructured and inconsistent data. You have good intuition for how numbers in a table map to the real world.
  • Are passionate about solving sustainability challenges and excited to contribute to doing so at a fast paced startup. You learn quickly and operate well in ambiguity.
FAQ

Where does Watershed work?

We have hub offices in San Francisco, New York and London, and some remote team members in the US and EU. Most of our jobs need to be in San Francisco / New York / London, but certain jobs are open to being remote and will be specifically noted on the jobs page and in the job description.

What's the interview process like?

It starts the same for every candidate: getting to know the team members through 1 to 2 conversations about Watershed, your experience, and your interests. Next steps can vary by role, but usual next steps are a skill or experience screen (e.g. a coding interview for an engineer, a portfolio review for a designer, deeper experience call for other roles) which leads to a virtual or in person interview panel after that if the screens go well. We prioritize transparency and lack of surprise throughout the process.

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