About Pivotal

We’re a fast-moving nature tech startup using on-the-ground biodiversity data to deliver accurate, multi-source insights into ecosystem condition. Our mission is to make ecosystem resilience a driver of corporate success, through accurate, truthful nature intelligence. We enable decision-makers to understand the true condition of ecosystems, track changes over time, confidently act on resilience risk, make better decisions about the deployment of capital, and see the outcomes of their actions - forging a tight link between corporate profitability and healthy ecosystems.

Our customers are those corporates and financiers who need accurate ecosystem condition intelligence because their profitability depends on it. That means large companies with highly nature-dependent commodity supply chains, and the financial institutions who invest in and lend to them.

Role purpose

Invent and validate new quantitative and geospatial methods that advance how we measure ecosystem condition at very large scales. Prototype data pipelines and work with software engineers to operationalise them. Ideal for someone from academia who’s excited to ship real-world impact and has at least some exposure to commercial settings.

What you’ll deliver

• Novel, evidence-based, quantitative approaches that materially improve our metrics and methods for assessing ecosystem condition across millions of hectares of productive land.
• Reproducible research assets: code, methods notes, validation studies, and uncertainty quantification.
• Prototypes that can be turned into production pipelines with the data engineering team.
• Clear communication of scientific choices to internal and external stakeholders.

Responsibilities

• Develop and test novel ideas that measure the properties of ecosystems at very large scales, such as commodity production landscapes. Including sampling design, statistical models, geospatial data pipelines, and ecosystem-level metrics.
• Design and run research projects aligned to product needs (problem framing, study design, success criteria).
• Carry out, and advance, the processing, aggregation, testing and analysis of multiple primary data sources, including acoustic data, near-ground imagery, and geospatial data.
• Quantify uncertainty and propagate it through to user-facing metrics and decisions.
• Collaborate closely with engineers to productionise research (clean code, tests, data versioning).
• Conduct literature reviews; maintain a living evidence base.
• Provide technical support and expertise across the business on ecological science and quantitative methods, including supporting the commercial team with new and existing commercial clients.
• Produce research summaries for multiple internal audiences, and externally shareable white papers/briefs.
• Where appropriate, present work to clients/partners and contribute to grants or collaborations.

Required skills & experience

• PhD in a quantitative field of ecology or the natural sciences (e.g., Quantitative Ecology, Population Ecology, Applied Statistics, Physical Geography, Environmental Science, Climate Science, etc) or a demonstrably equivalent track record.
• Experience in a diverse range of complex, quantitative methods, e.g., GLM/GAM, mixed/hierarchical models, Bayesian inference, spatiotemporal modelling, occupancy modelling, ML models.
• Proficient in Python for scientific computing & geospatial analysis (Pandas, GeoPandas, rasterio, xarray, scikit-learn).
• Experience working with large, messy ecological and geospatial datasets.
• Able to produce high quality code and clearly documented research outcomes & methods, including experience with coding best practices (e.g., version control (Git), unit testing, CI/CD).
• Excellent communication skills: able to collaborate cross-functionally with scientists, engineers and commercial customers, and communicate complex technical concepts to different audiences.
• Organised, curious & innovative: you like asking good questions, coming up with novel ideas, and seeing the bigger picture.
• Empathy for both user needs and scientific integrity. Able to hold the tension between ambition, perfectionism, and realism.
• Comfortable working in a fast-paced, startup-style environment. An appetite to move fast and ship to production.

Desirable

• Some commercial exposure (e.g., industry role or partnership, nature tech startup role, consultancy, etc.).
• Experience with Google Earth Engine.
• Experience with the R programming language (tidy verse, shiny)
• Experience with cloud computing (Azure, AWS and/or GCP).
• Familiarity with ecological data types & biodiversity metrics (e.g., acoustic data, imagery data, species observations, occupancy modelling, abundance estimation, etc.).
• Experience building production-ready data pipelines for the analysis of nature data.

Why Join Us?

• Impact – Work on data-driven solutions that regenerate biodiversity at scale.
  

• Innovation – Be part of a cutting-edge nature-tech company.
  

• Hands-on Learning – Gain hands-on experience in a range of areas, including tech and AI.
  

• Mission-Driven – Work with a team passionate about nature and technology.
  

• Remote-first – Flexibly work from where you prefer, but must be within 3 hours of GMT.
  

We are a globally distributed, fully remote company, and we are committed to a market-led approach to compensation based on the location in which the employee lives and works. A guiding range for this role is £60,000-80,000 per annum (or equivalent in local currency), dependent on geography and experience. Start date is as soon as possible.

We’re an equal opportunity employer. All applicants will be considered for employment without attention to age, ethnicity, religion or belief, sex, sexual orientation, gender identity, family or parental status, national origin, veteran, neurodiversity, disability status or any other basis as protected by applicable law.