Senior Data Scientist, KoBold Metals

• Technical skills, including extensive experience with:
• 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
• Working with a broad range of types of data including data from physical systems
• Geospatial analyses and visualizations
• Technical knowledge:
• Broad skills in and knowledge of applied statistics
• Substantial understanding of Machine Learning algorithms
• Training and work experience:
• An advanced degree in the physical sciences, engineering, computer science, or mathematics
• Post-degree work experience as a data scientist or data engineer. KoBold is willing to consider candidates at the Data Scientist, Senior Data Scientist, or Staff Data Scientist levels, with commensurate compensation, however recent bachelor’s/master’s candidates are unlikely to be competitive
• Work practices and motivation:
• Ability to take ownership and responsibility of large projects
• Enjoys constantly learning such that you are and driving insights and innovations
• Ability to explain technical problems to and collaborate on solutions with domain experts who aren’t software developers
• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities
• Ability to independently prioritize multiple tasks effectively

Desirable

• 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