Senior Applied AI Scientist

DeepRec.ai

London

Remote

GBP 60,000 - 80,000

Full time

14 days+

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Benefits offered by this job

32 days paid holiday

Extra day off on your birthday

Pension scheme

Enhanced gender-neutral parental leave

Mental wellbeing support

Company laptop + home working setup allowance

Job summary

A nature-based solutions startup is seeking a Senior Applied AI Scientist to lead the development of ML solutions addressing climate and biodiversity challenges. The ideal candidate has a strong background in applied machine learning and a passion for environmental impact. This remote-first role offers flexible working and generous benefits, including 32 days of paid holiday and a supportive culture.

Qualifications

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).
Advanced degree (MSc or PhD) in a related field.

Responsibilities

Design, build, and scale machine learning models using environmental and observational data.
Apply advanced causal inference techniques such as Bayesian Neural Networks.
Work cross-functionally with science, engineering, and product teams.
Mentor junior team members and foster best practices in applied ML.

Skills

Applied machine learning

Bayesian statistics

Causal inference

Python

ML frameworks (e.g., PyTorch)

Cloud infrastructure (e.g., AWS)

Clear communication

Adaptive mindset

Education

MSc or PhD in Computer Science, Statistics, Economics, Physics, Mathematics

Tools

AWS

GCP

TorchGeo

Rasterio

Geopandas

Xarray

Dask

Job description

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