- Architect, implement, and maintain foundational scientific computing libraries that will be used in KoBold’s mineral exploration analyses.
- Build tooling to increase the velocity of our machine learning progress, including enabling rapid prototyping in Jupyter notebooks; build experimentation, evaluation, and simulation frameworks; turning successful R&D into robust, scalable ML pipelines; and organizing models and their outputs for repeatability and discoverability.
- In collaboration with data scientists, build models to make statistically valid predictions about the locations of economic concentrations of ore metals within the Earth’s crust.
- Apply–and coach team members to use–engineering best practices such as writing robust, testable and composable code.
- Collaborate with data scientists, geoscientists and engineers to invent the modern scientific computing stack for mineral exploration.
- Travel occasionally to exploration sites around the world, approximately twice per year, to observe the impact of scientific computing and design new technologies.
PythonMachine LearningNumpy+2 more