Data Scientist

Climate Policy Radar is a non-profit organisation building open databases and research tools so people can discover and understand complex information, in particular long-text documents, on climate, nature and development. Our data and tools help governments, researchers, international organisations, civil society, and the private sector to understand and advance effective climate policies and deploy climate finance. Harnessing data science and AI, and pioneering the application of natural language processing to this domain, our work renders previously unstructured, siloed data more readable and accessible.

We are a team of ~30 technologists and climate policy experts who care a lot about the ‘how’ (our values and culture) as well as the ‘what’. As part of that, we have embraced a flexible, hybrid approach to work, including a 4 day workweek.

We are looking for a Data Scientist to join our data science team.

This is a growth hire, adding to Climate Policy Radar’s data science capacity. Reporting to the Head of Data Science, you'll work closely with our existing team of data scientists to analyse our datasets, build tools to deepen that analysis, and then product ionise those tools for users to leverage in their own research.

As a skilled generalist within NLP, you'll work on a range of interesting problems here, with a range of interesting people! You'll collaborate with climate experts to understand the problems we need to solve, and with software engineers to take your work from research through to production.

Climate Policy Radar's engineering org is split into a few functional teams: programmes (domain experts), platform (high-quality data sharing), application (user-facing tools), and data science (models and evaluation). Despite those splits, most of our work is cross-functional.

The data science team builds the models that power our search engine, classifiers, and LLM workflows, and the evaluation frameworks that keep them honest. Our work directly informs policy decisions, so we care a lot about evaluation, monitoring, and minimising bias. We're research-informed but production-focused, and we default to working in the open by publishing datasets, models, and papers wherever we can.

We also share what we've learned through blogs, papers, and public talks.

The vast majority of the data science team’s work is written in Python, so we’ll expect you to be a fluent reader/writer!

In addition, here’s an (unordered) list of tools which we work with regularly. We’re always open to new suggestions of tools you've had success with, but familiarity with these tools will help you integrate with our existing stack:

• ML & NLP: PyTorch, Huggingface Transformers, Pydantic AI, Pandas, Spacy
• APIs & backend: Python, FastAPI, Pydantic
• Testing, evaluation & monitoring: Pytest, Hypothesis, Weights & Biases, Posthog
• Development tools: Git Hub, Cursor, Claude Code

Climate Policy Radar's core product is a search engine for 30,000+ climate documents. That engine is supported by a set of single-class classifiers which detect mentions of important climate concepts (e.g. flooding, or deforestation). In your role, you're likely to:

• Work closely with the programmes and platform teams to help shape our research & development priorities
• Develop, evaluate, and deploy state-of-the-art NLP models which extract structured information from climate-related documents, with an emphasis on small, efficient architectures
• Help to build and deploy APIs which demonstrate new model capabilities, for other teams to prototype around
• Contribute to building evaluation harnesses for search relevance, classifier accuracy, data quality, etc, which allow other teams to reliably and independently iterate on work
• Implement approaches for data labelling or sampling to more efficiently create training and evaluation datasets
• Analyse real user search logs and behavioural analytics to describe their journeys across our site and understand their needs.
• Research using LLMs to automate tedious parts of the climate policy research process, and partner with experts to build evaluation…