Direct message the job poster from DeepRec.ai
Senior Applied AI Scientist
Location: UK-based, remote-first (with monthly optional meetups in London)
Start date: ASAP
Eligibility: Must have the right to work in the UK
Overview
DeepRec.ai has the pleasure of partnering with a remote-first NbS startup as they look to hire a Senior Applied AI Scientist to lead the development of ML-driven solutions that scientifically quantify the real-world impact of nature-based interventions. You'll join a multidisciplinary team of AI scientists, engineers, and environmental experts tackling one of the biggest challenges of our time: building trusted, scalable tools for climate and biodiversity action.
The Culture
- Shared purpose, no ego.
- Remote-first with flexible working hours, built on trust.
- Monthly team meetups at a London-based office (Highbury).
- Clear communication, fast iteration, and support over silos.
- A culture that thrives on ambiguity, feedback, and a growth mindset.
What You’ll Do
- Design, build, and scale machine learning models using environmental and observational data.
- Apply advanced causal inference techniques such as Bayesian Neural Networks, Gaussian Processes, Difference-in-Differences, and Synthetic Control methods.
- Leverage foundation models (e.g. Prithvi, Clay) and transformers to extract insights from complex datasets.
- Work cross-functionally with science, engineering, and product teams to embed models into real-world pipelines.
- Communicate scientific and technical concepts clearly to both technical and non-technical audiences.
- Stay current with the latest developments in AI and environmental science, integrating relevant innovations into production.
- Mentor junior team members and foster best practices in applied ML.
What You Bring
- Strong background in applied machine learning, bayesian statistics, and causal inference.
- Proficiency in Python and ML frameworks such as PyTorch.
- Experience with cloud infrastructure (e.g., AWS, GCP).
- A clear, concise communication style – clear examples given when asked, not word salad.
- An adaptive mindset and comfort working in fast-changing environments.
- A deep motivation to contribute to climate and ecological impact.
- An advanced degree (MSc or PhD) in Computer Science, Statistics, Economics, Physics, Mathematics, or a related field.
Nice to Have
- Experience working with geospatial or spatial-temporal data.
- Experience with remote sensing datasets (e.g., Landsat, Sentinel, SAR).
- Familiarity with TorchGeo or TerraTorch.
- Experience with Rasterio, Geopandas, Xarray, or Dask.
- Previous collaboration with academic or scientific research communities.
- Publications in peer-reviewed journals or conferences.
Benefits
- Remote-first and flexible hours
- 32 days paid holiday (including bank holidays, fully flexible)
- Extra day off on your birthday
- Pension scheme
- Enhanced gender-neutral parental leave
- Spill mental wellbeing support
- Company laptop + home working setup allowance
Seniority level
- Not Applicable
Employment type
- Full-time
Job function
- Science
- Industries
- Research Services
Direct message the job poster from DeepRec.ai
Senior Applied AI Scientist
Location: UK-based, remote-first (with monthly optional meetups in London)Start date: ASAPEligibility: Must have the right to work in the UK
Overview
DeepRec.ai has the pleasure of partnering with a remote-first NbS startup as they look to hire a Senior Applied AI Scientist to lead the development of ML-driven solutions that scientifically quantify the real-world impact of nature-based interventions. You’ll join a multidisciplinary team of AI scientists, engineers, and environmental experts tackling one of the biggest challenges of our time: building trusted, scalable tools for climate and biodiversity action.
The Culture
- Shared purpose, no ego.
- Remote-first with flexible working hours, built on trust.
- Monthly team meetups at a London-based office (Highbury).
- Clear communication, fast iteration, and support over silos.
- A culture that thrives on ambiguity, feedback, and a growth mindset.
What You’ll Do
- Design, build, and scale machine learning models using environmental and observational data.
- Apply advanced causal inference techniques such as Bayesian Neural Networks, Gaussian Processes, Difference-in-Differences, and Synthetic Control methods.
- Leverage foundation models (e.g. Prithvi, Clay) and transformers to extract insights from complex datasets.
- Work cross-functionally with science, engineering, and product teams to embed models into real-world pipelines.
- Communicate scientific and technical concepts clearly to both technical and non-technical audiences.
- Stay current with the latest developments in AI and environmental science, integrating relevant innovations into production.
- Mentor junior team members and foster best practices in applied ML.
What You Bring
- Strong background in applied machine learning, bayesian statistics, and causal inference.
- Proficiency in Python and ML frameworks such as PyTorch.
- Experience with cloud infrastructure (e.g., AWS, GCP).
- A clear, concise communication style – clear examples given when asked, not word salad.
- An adaptive mindset and comfort working in fast-changing environments.
- A deep motivation to contribute to climate and ecological impact.
- An advanced degree (MSc or PhD) in Computer Science, Statistics, Economics, Physics, Mathematics, or a related field.
Nice to Have
- Experience working with geospatial or spatial-temporal data.
- Experience with remote sensing datasets (e.g., Landsat, Sentinel, SAR).
- Familiarity with TorchGeo or TerraTorch.
- Experience with Rasterio, Geopandas, Xarray, or Dask.
- Previous collaboration with academic or scientific research communities.
- Publications in peer-reviewed journals or conferences.
Benefits
- Remote-first and flexible hours
- 32 days paid holiday (including bank holidays, fully flexible)
- Extra day off on your birthday
- Pension scheme
- Enhanced gender-neutral parental leave
- Spill mental wellbeing support
- Company laptop + home working setup allowance
Seniority level
- Not Applicable
Employment type
- Full-time
Job function
- Science
- Industries
- Research Services
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