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KoBold Metals

AI-driven mineral exploration for critical metals

Data Scientist

Data ScienceConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Extensive experience with Python data science packages and general software engineering practices.
  • Experience with collaborative software development using git, and familiarity with software engineering best practices such as unit test and integration test suites and continuous integration/continuous delivery pipelines.
  • Experience with cloud computing resources.
  • Experience building predictive models, applying them to different problems, and evaluating and interpreting the results.
  • Experience working with data from a variety of physical systems.
  • Experience with geospatial analyses and visualizations.
  • Broad skills in and knowledge of applied statistics and Bayesian inference.
  • Substantial understanding of machine learning algorithms.
  • An advanced degree in the physical sciences, engineering, computer science, or mathematics.
  • At least 4 years of work experience post-PhD or 8 years post-MS, ideally as a data scientist or data engineer.
  • Experience leading technical teams to apply novel scientific approaches to core business problems.
  • Ability to take ownership and responsibility of large projects.
  • Ability to explain technical problems to and collaborate on solutions with domain experts.
  • Ability to communicate and collaborate on a cross-functional team.
  • Ability to independently prioritize multiple tasks effectively.
  • Intellectual curiosity and eagerness to learn about mineral exploration, particularly geology.
  • Willingness to work directly with geologists in the field.
  • A valid passport and willingness to travel to observe work at Mingomba or an exploration site around the world.

Responsibilities

  • Help develop KoBold’s proprietary software exploration tools.
  • 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 targeting efforts and inform acquisition and exploration decisions.
  • Present to and collaborate with external partners and stakeholders.
  • Create 2D and 3D geologic predictions, identify exploration targets, design field programs to collect data, and use that data to reduce uncertainty in predictions and guide the next phase of field work.
  • Build data tools to solve scientific problems and help build these tools from the ground up.

Desired Qualifications

  • Experience creating machine learning models on geospatial data.
  • Experience with geostatistics.
  • Experience with image processing or computer vision.
  • Experience with distributed computing applications for machine learning and other computations.

About the Company

KoBold Metals

AI-driven mineral exploration for critical metals

KoBold Metals uses artificial intelligence to accelerate the discovery of lithium, cobalt, copper, and nickel deposits for clean energy. Its main product is an AI-driven mineral exploration program that combines a proprietary EOI tool with large-scale data about the Earth’s crust to decide which data to collect at each exploration step and reduce uncertainty. It stands out from traditional explorers through heavy data gathering, a multidisciplinary team, and a data-powered, repeatable scientific process. Its goal is to locate and sell mineral deposits to clean-energy companies, speeding the supply of critical minerals for batteries and renewable energy technologies.

Company size
201-500
Company stage
Late Stage VC
Total funding
$1.1B
Headquarters
Berkeley, CA
Founded
2018

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