What are the responsibilities and job description for the Lead Geohazard Scientist — Machine Learning & Predictive Modeling position at Cascade Geomatics?
Company Description Cascade Geomatics enables operators in Rail, Mining, and Departments of Transportation (DOT) to move from reactive maintenance to proactive geohazard risk management. The company’s terrain intelligence platform transforms complex mountain environments and infrastructure corridors into live 3D digital twins for continuous monitoring. By integrating ruggedized field sensing with advanced analytics, Cascade Geomatics detects subtle environmental changes at an early stage. This approach allows forecasting of avalanche, rockfall, and landslide hazards before they become catastrophic events, helping clients improve safety, reliability, and operational efficiency.
Role Description The Lead Geohazard Scientist — Machine Learning & Predictive Modeling is a full-time, on-site role based in Corvallis, OR. This role leads the design, development, and deployment of predictive models that quantify geohazard risk across rail lines, mine sites, and transportation corridors. Day-to-day responsibilities include analyzing large geospatial and sensor datasets, building and validating machine learning models, and integrating predictive outputs into the company’s terrain intelligence and digital twin platform. The Lead Geohazard Scientist collaborates closely with field sensing teams, software engineers, and product managers to refine data pipelines, improve hazard forecasting accuracy, and translate scientific insights into actionable tools for clients. The role also involves mentoring team members, contributing to technical roadmaps, and communicating findings to both technical and non-technical stakeholders.
Qualifications
Role Description The Lead Geohazard Scientist — Machine Learning & Predictive Modeling is a full-time, on-site role based in Corvallis, OR. This role leads the design, development, and deployment of predictive models that quantify geohazard risk across rail lines, mine sites, and transportation corridors. Day-to-day responsibilities include analyzing large geospatial and sensor datasets, building and validating machine learning models, and integrating predictive outputs into the company’s terrain intelligence and digital twin platform. The Lead Geohazard Scientist collaborates closely with field sensing teams, software engineers, and product managers to refine data pipelines, improve hazard forecasting accuracy, and translate scientific insights into actionable tools for clients. The role also involves mentoring team members, contributing to technical roadmaps, and communicating findings to both technical and non-technical stakeholders.
Qualifications
- Strong skills in machine learning and predictive modeling, including experience with supervised and unsupervised methods and time-series or spatiotemporal analysis.
- Proficiency working with geospatial data, digital elevation models, and terrain or environmental datasets, ideally in the context of geohazards such as avalanches, rockfall, or landslides.
- Advanced programming skills in languages commonly used for scientific computing and data science (such as Python, R, or Julia), including experience with relevant ML and data processing libraries.
- Experience architecting and optimizing data pipelines, integrating sensor data streams, and deploying models into production environments in collaboration with engineering teams.
- Background in earth sciences, geomatics, geotechnical engineering, or a related field, supported by a graduate degree or equivalent professional experience.
- Demonstrated ability to design research plans, validate models rigorously, and communicate complex technical results clearly to cross-functional teams and external partners.
- Experience leading or mentoring multidisciplinary teams, with a collaborative approach and commitment to inclusive, evidence-based decision-making.
- Familiarity with cloud platforms, version control, and modern software development practices; experience in rail, mining, or transportation sectors is beneficial.