Senior Applied AI Scientist

ZipRecruiter; London; Remote; GBP 125,000 - 150,000; Full time

Job summary

A remote-first NbS startup is seeking a Senior Applied AI Scientist to lead the development of machine learning models for environmental impact. The successful candidate will have extensive experience in AI and the desire to contribute to climate solutions. Responsibilities include designing ML models and cross-functional collaboration. The role is flexible with 32 days of paid holiday and various employee benefits.

Benefits

Flexible working hours

32 days paid holiday

Pension scheme

Laptop + home working setup allowance

Qualifications

• Strong background in applied machine learning and causal inference.
• Proficiency in Python and machine learning frameworks.
• Experience with cloud infrastructure like AWS or GCP.

Responsibilities

• Design and scale machine learning models using environmental data.
• Apply causal inference techniques to analyze data.
• Collaborate with teams to implement models in real-world settings.

Skills

Applied machine learning

Bayesian statistics

Causal inference

Python

ML frameworks (e.g., PyTorch)

Education

Advanced degree (MSc or PhD) in relevant field

Tools

AWS

GCP

Job description

Job DescriptionSenior 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-, 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 -neutral parental leave
• Spill mental wellbeing support
• Company laptop + home working setup allowance