Applied Scientist

About the company: 

The mining industry has steadily become worse at finding new ore deposits, requiring >10X more capital to make discoveries compared to 30 years ago. The easy-to-find, near-surface deposits have largely been found, and the industry has chronically under-invested in new exploration technology, relying on the manual techniques of yesteryear – even as demand accelerates for copper, lithium, and other metals to build electric vehicles, renewable energy, and data centers.

KoBold builds AI models for mineral exploration and deploys those models—alongside our novel sensors—to guide decisions on KoBold-owned-and-operated exploration programs. In the six years since founding, KoBold has become by far both the largest independent mineral exploration company and the largest exploration technology developer. Our data scientists and software engineers, who come from leading technology companies, jointly lead exploration programs with our renowned exploration geologists.

KoBold has proven its first discovery with materially less capital than the industry average and found one of the best copper deposits ever discovered: the copper is far more concentrated than the global average of copper mines, and this asset alone is expected to generate meaningful revenue for decades. KoBold has a portfolio of more than 60 other projects, each of which has the potential for another high-quality discovery.

KoBold is privately held; investors include institutional asset managers T. Rowe Rice and Canada Pension Plan Investments; technology venture capitalists Andreessen Horowitz, Breakthrough Energy Ventures, BOND Capital, and Standard Investments; and natural resources companies Equinor, BHP, and Mitsubishi.

About The Role:

In this role, you will develop state of the art instruments to collect data to guide our exploration programs. You will simulate, characterize, and calibrate sensor performance and design new data acquisition systems. Working with vendors and partners, you will build hardware and work with our operations staff to deploy it in the field. 

With this sensor data and other data sources at KoBold, you will build 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 help build a worldwide dataset that underlies our exploration program, with careful attention to identifying and quantifying uncertainty in the data and in our predictions. You will be creating models and developing software to accelerate discovery of critical battery metals.

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. Working with your geoscience colleagues, you will identify new opportunities and technologies for geophysical data collection, create 2D and 3D geologic predictions, identify exploration targets, design field programs to collect data, and use that data to reduce the uncertainty in our predictions and guide the next phase of field work.

Ultimately, your role is to help KoBold make valuable discoveries by building and deploying next generation hardware and using data tools to solve scientific problems. As one of the early members of this team, you will help build these tools from the ground up.

Responsibilities of this position include:

Technical execution

• 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 

Qualifications

• Demonstrated ability to quickly absorb and synthesize complex information, with a track record of high intellectual rigor in a professional setting.
• Exceptional curiosity and eagerness to learn, with a proactive approach to exploring new concepts and technologies.
• A successful track record of developing complex equipment with cross-disciplinary teams and vendors.

Technical skills, including extensive experience with:

• 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

Work practices and motivation:

• Ability to take ownership and responsibility of large projects.
• Intellectual curiosity and eagerness to learn about all aspects of mineral exploration, particularly in the geology domain. Open to working directly with geologists in the field. Enjoys constantly learning such that you are driving insights and innovations.
• Ability to explain technical problems to and collaborate on solutions with domain experts who aren’t software developers. A strong communicator who enjoys working with colleagues across the company.
• Excitement about joining a fast-growing early-stage company, comfort with a dynamic work environment, and eagerness to take on a range of responsibilities.
• Keen not just to build cool technology, but to figure out what technical product to build to best achieve the business objectives of the company.
• Ability to independently prioritize multiple tasks effectively.

It is also helpful but not required to have experience with:

• Remote sensing
• Creating machine learning models on geospatial data
• Image processing or computer vision
• Project and team management

KoBold Metals is an equal opportunity workplace and an affirmative action employer. We are committed to equal employment opportunity for people of any race, color, ancestry, religion, sex, gender identity, sexual orientation, marital status, national origin, age, citizenship, disability, or veteran status.

This position is Full-time Exempt

The US base salary range for this full-time exempt position is $200,000 - $230,000

Location: Remote, Candidates can be located anywhere in the United States or Canada. All candidates must be legally authorized to work in the United States or Canada. 10-20% travel required.