Data Scientist -Atmospheric Science

HRB; Canada; Remote; CAD 90,000 - 130,000; Full time

Job summary

Join a pioneering startup focused on wildfire prevention as a Data Scientist. You will leverage machine learning, geospatial data, and your data science expertise to develop predictive models that tackle environmental challenges related to wildfires. This role offers a groundbreaking opportunity to make significant contributions to climate resilience and protect ecosystems.

Qualifications

• 5+ years in data science with an environmental focus.
• Experience in developing predictive models.
• Strong skills in Python or R and geospatial analysis.

Responsibilities

• Design and build machine learning models for wildfire prediction.
• Develop real-time data streaming and analysis pipelines.
• Collaborate with team members to refine models.

Skills

Data Science

Predictive Analytics

Machine Learning

Geospatial Analysis

Data Visualization

Tools

Python

R

QGIS

AWS

TensorFlow

Job description

Your opportunity

Founded in 2024 our client is an early-stage startup with a pioneering approach to wildfire prevention, leveraging novel, predictive models to prevent catastrophic wildfires ignited by lightning over (and near) high-risk areas. Lightning strikes account for 60% of wildfires in Canada, resulting in 93% of the burned area and emissions—their technology focuses on reducing wildfire occurrences and emissions by suppressing lightning strikes before they ignite these fires.

Their work combines cutting-edge geospatial data analysis, machine learning, and computer vision to create a first-of-its-kind solution that anticipates and prevents lightning-induced wildfires at their source. This is a rare opportunity to build entirely novel capability and to contribute to a critical area of research that’s largely uncharted.

As a Data Scientist on their team, you’ll be instrumental in building models that can predict lightning strikes and assess wildfire risks with unparalleled precision. You’ll work directly with large datasets—from satellite imagery to real-time weather/atmospheric data and historical fire records—transforming complex data into actionable insights to prevent wildfire ignition. This role is a unique, groundfloor opportunity to build, test, and refine predictive models that address one of the world’s most pressing environmental threats, shaping a solution that benefits hundreds of millions of people worldwide and saves businesses and governments billions of dollars annually.

Key responsibilities

• Data modelling & prediction: Design, build, and deploy machine learning models to predict lightning events and assess fire risk, utilizing large-scale environmental datasets

• Data processing & integration: Work with diverse data sources, including weather data, satellite imagery, and historical fire records, to extract valuable insights and develop effective predictive models

• Real-time monitoring & alerting: Develop and maintain pipelines for real-time data streaming, analysis, and alerting to detect potential wildfire threats before they escalate

• Collaboration & innovation: Collaborate with scientists, engineers, leadership and the product organization to refine models and develop actionable strategies for wildfire mitigation

• Visualization & communication: Present complex data findings in a clear, compelling way to both technical and non-technical stakeholders, supporting informed, data-driven decisions

What we’re looking for

• Experience: 5+ years in data science, with a focus on environmental or geospatial data, and proven experience developing models for predictive analytics

• Technical proficiency: Strong skills in Python or R, with experience in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and data manipulation libraries (Pandas, NumPy)

• Geospatial & satellite data expertise: Proficiency with geospatial analysis, including satellite imagery processing and tools like QGIS, GeoPandas, and Rasterio

• Atmospheric modelling: Familiarity with numerical atmospheric models (e.g., WRF, ECMWF) to support predictive capabilities

• Big data handling: Experience with large datasets and cloud-based data platforms (e.g., AWS, Google Cloud), and familiarity with distributed computing tools such as Spark

• Passion for impact: Strong motivation to use data science for social and environmental good, with an interest in climate technology and environmental resilience

Why join?

With our client, you’ll have the chance to be part of something groundbreaking. This is more than a job—it’s a unique opportunity to advance novel technology and contribute to an emerging area of research with untapped potential. Join a team that values curiosity, impact, and innovation, and help shape a solution that could redefine how we protect our planet from wildfire devastation