Applied Scientist

Physical measurement and data analysis systems that use phenomena such as optics, electromagnetism, radiation, and gravity. Applying scientific knowledge to identify and prototype emerging technologies Systems integration and data acquisition. Python’s data science packages and general software engineering practices. Collaborative software development (git), and familiarity with software engineering best practices like unit test / integration test suites, and CICD pipelines. SQL, as well as familiarity with non-relational databases. Cloud computing resources. Building a wide variety of predictive models, applying them to different problems, and evaluating and interpreting the results. Data analysis, physics analysis, and applied statistics on a broad range of types of data including data from physical systems. Capacity to dive deep on novel challenging problems in applying ML to mineral exploration, including understanding a complex domain of geology and mineral exploration practices as well as working with limited, disparate and noisy data sources Experience deploying sensors in the field

Design, develop, and deploy new mineral exploration data collection instruments and methods. Help develop KoBold’s proprietary software exploration tools. Find and curate a wide variety of geospectral, geophysical, geochemical, geologic, and geographic data and integrate it into KoBold’s proprietary data system. Build models to make statistically valid predictions about the locations of compositional anomalies within the Earth’s crust. Create effective visualizations for evaluating model performance and enabling rapid interaction with the underlying data and key features. Develop and apply a range of data processing, statistical, and physics-based techniques to geoscientific data — from computer vision to geophysical inversions — and use the results to guide our targeting efforts and inform our acquisition and exploration decisions. Present to and collaborate with our external partners and stakeholders. Push the state of the art in analysis capabilities by implementing statistically rigorous spatially aware clustering, anomaly detection, and other analysis methods Collaborate with data scientists, geoscientists and engineers to invent and deploy algorithms that combine large and complex data sets for mineral exploration and discoveries