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, using geophysical data and incorporating satellite imagery, weather station data, and historical fire records to improve forecasting accuracy

• Data processing & integration: Work with diverse data sources, including satellite observations, weather sensors, and geospatial data, to develop pipelines for ingesting, cleaning, and normalizing large-scale, real-time geophysical datasets

• Statistical analysis & model validation: Apply robust statistical frameworks to assess model performance, quantify uncertainty in predictions, conduct hypothesis testing, and evaluate the reliability of forecasting methodologies

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

• Learning plan development & iterative improvement: Lead structured learning plans to refine prediction methodologies, assess model effectiveness, identify gaps, and adjust strategies to improve data collection and model performance

• Collaboration & communication: Collaborate with scientists, engineers, and product teams to refine models, and present complex data findings in a clear, compelling way to both technical and non-technical stakeholders

What we’re looking for

• Education: Ph.D. or master’s in statistics or mathematics (required) with cross‑disciplinary training in data science, machine learning, or a related quantitative field; background in atmospheric science, physics, or fluid dynamics is a plus

• Experience: At least five years of experience applying advanced statistical methods to real-world datasets, including work with geophysical or environmental data (e.g., lightning, weather, fire risk, satellite imagery); demonstrated expertise in time-series forecasting, geospatial modelling, probabilistic reasoning, and uncertainty quantification

• Technical proficiency: Deep proficiency in Python (e.g., NumPy, Pandas, SciPy, scikit-learn, TensorFlow/PyTorch) with a strong foundation in statistical modelling, Bayesian inference, and data-driven hypothesis testing; experience working with remote sensing data (e.g., MODIS, GOES, VIIRS, Sentinel), GIS tools (e.g., QGIS, Google Earth Engine), and geospatial databases (e.g., PostGIS) is highly valued

• Cloud computing: Experience working within modern cloud environments (e.g., AWS, Google Cloud) and leveraging distributed computing frameworks (e.g., Spark) to scale complex analytical workloads

• Research and iteration: Demonstrated ability to design structured learning plans, validate statistical models, and iterate on experimental frameworks to drive improvements in predictive performance and model robustness

• Collaboration & communication: Exceptional communication skills, critical thinking, and the ability to translate statistical insights into actionable outcomes. Comfortable working across disciplines to bridge data science and domain expertise

• Adaptability: A rigorous yet creative approach to applying statistical science in dynamic environments. Motivated by impact and continuous learning, with a strong interest in improving forecasting and decision-making in geophysical risk contexts

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.

Interested in learning more? 

Please upload your resume or a .pdf export of your LinkedIn profile using the following link or send your resume or LinkedIn profile URL to talent@lutrapartners.com with “Senior Data Scientist” as the subject, and one of our partners will be in contact shortly!