About Air Aware Labs

Air Aware Labs is an early-stage startup transforming environmental health analytics by integrating AI-driven pollution exposure modelling with real-time health insights. Our flagship mobile application, AirTrack, empowers individuals to make data-informed choices to reduce their air pollution exposure. We also partner with organisations across fitness, healthcare, employee wellbeing, and urban planning—providing actionable air quality insights that enables real-world change.

We are growing quickly and seeking an AI Scientist (open to AI, Data Science, or Environmental Health Analytics backgrounds) to advance our personalised exposure modelling and pollution-health impact analytics. This is an opportunity to make a tangible difference by leveraging data science at the intersection of air quality and public health.

The Role

As an AI Scientist, you will focus on building and refining our air pollution exposure models, developing data-driven insights for low-pollution routing, and quantifying the health implications of air pollution. You’ll work closely with a small cross-functional team (and external partners) to ensure our solutions are both scientifically robust and practical for real-world deployment.

Key Responsibilities

1. Pollution Exposure Modelling
○ Analyse large pollution datasets and merge them with mobility data to enhance personalised exposure insights.
○ Develop and refine algorithms for low-pollution routing and exposure heatmaps.
○ Benchmark models against data from Google’s Air Quality API, sensor networks, and regulatory sources.

2. Health Impact Analysis
○ Collaborate with internal and external public health experts to incorporate evidence-based metrics of pollution’s effect on individual health and wellbeing.
○ Translate air pollution exposure data into actionable health insights for corporate, governmental, and consumer-facing applications.

3. Data Science & Integration
○ Build and optimise machine learning models to power real-time, personalised pollution tracking.
○ Work alongside software engineers to integrate these models into our products, ensuring efficient and scalable deployment.
○ Contribute to broader data strategies, helping shape best practices and potential future leadership in our data science function.

Who You Are
● Qualifications
○ PhD (or MSc with at least 2 years of startup experience) in AI, Data Science, Environmental Science, Public Health, or a related field.
○ Proven expertise in AI/ML and data science, ideally within environmental analytics or epidemiological modelling.

● Technical Skills
○ Strong programming (e.g. Python, R) and analytical skills (geospatial analytics, large datasets).
○ Familiarity with GCP cloud-based workflow is a plus.

● Mindset
○ Mission-driven, with a passion for using data and AI to improve public health.
○ Eager to collaborate cross-functionally and potentially grow into a leadership role as the data science function expands.

Bonus Points ✨
● Experience with route mapping, wearable health data, or mobility analytics.
● Prior contributions to scientific research or industry projects in air quality, epidemiology, or health AI.
● Experience deploying ML models in a production environment (CI/CD, containerisation, scalable infrastructure).

Why Join Us?
🌍 Make an Impact – Your data-driven insights will directly help individuals, businesses, and governments reduce personal exposure to air pollution.
🚀 Growth Potential – As our first dedicated AI Scientist, you’ll shape our data and analytics strategy.
💰 Stock Options – Share in Air Aware Labs’ success and growth.
🏡 Hybrid & Flexible Working – Collaborate in person 2–3 days a week, with the flexibility for remote work.
🌴 Annual Leave – 25 days of holiday plus bank holidays.
🌈 Inclusive Culture – We encourage applications from all backgrounds. If you’re excited about this role but don’t meet every requirement, please apply.

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📩 Interested? Apply now with your CV and a short cover letter outlining your expertise and motivation to will@airawarelabs.com and louise@airawarelabs.com.

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