Data Scientist - (Global)

Remote

Responsibilities

Find and curate 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 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.
Building predictive models, applying them to different problems, and evaluating and interpreting the results.

KoBold Metals is a mineral exploration company using AI to explore for the metals we need for our transition to a low-carbon economy.
Prior to joining KoBold, KoBold team members have made nearly 20 discoveries.
Our exploration programs are co-led by our geoscientists and data scientists, who develop exploration hypotheses, rigorously quantify uncertainty in our understanding of the subsurface, and design data collection programs that most effectively reduce uncertainty, drawing upon a large suite of proprietary exploration technology built by our data scientists and software engineers.
In this role, you will build predictive models and apply a wide range of scientific computing, statistical, and physics-based methods to find places where there is evidence of ore-forming processes at work and to predict the locations of ore-grade mineralization in 2D and 3D.
You will join an outstanding team of data scientists and engineers and will work closely with KoBold’s world-renowned geoscientists to incorporate our best understanding of the chemical and physical processes that create ore deposits.
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.