Data Scientist - Staff or Senior

Data Scientist (Senior Staff, Staff, & Senior roles available) About the company Aperture Cloud is a mineral exploration company using AI to explore for the metals we need for our transition to a low-carbon economy. KoBold’s business is discovering, defining, expanding, and developing mineral resources, and KoBold’s objective is to achieve a step-change improvement in exploration success: we aim to discover more tier 1 resources, faster, and with fewer failures. KoBold has a global portfolio of more than 50 exploration properties targeting nickel, copper, cobalt, and lithium, which range from 100%-owned to partnerships with both majors, junior explorers, and prospectors. Our team includes the best of the industry in exploration geoscience, data science, software engineering, operations, and business personnel. 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. Our field programs validate and improve the system and have demonstrated material improvements over conventional exploration methods KoBold is privately held and our investors include: institutional asset managers T. Rowe Rice and Canada Pension Plan Investments; technology venture capitalists Andreessen Horowitz, Bill Gates’s Breakthrough Energy Ventures, BOND Capital, Standard Investments, and Sam Altman’s Apollo Projects; and leading natural resources companies Equinor, Mitsubishi, and BHP. We are hiring a Data Scientists to help accelerate our mission. About the position 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 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 create models and develop 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 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 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 The Data Scientist will: 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 our targeting efforts and inform our acquisition and exploration decisions. Present to and collaborate with our external partners and stakeholders. Qualifications A great Data Scientist candidate will have: 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. Cloud computing resources. Building predictive models, applying them to different problems, and evaluating and interpreting the results. Data from a variety of physical systems. Geospatial analyses and visualizations. Technical knowledge: Broad skills in and knowledge of applied statistics and Bayesian inference Substantial understanding of machine learning algorithms Training and work experience: An advanced degree in the physical sciences, engineering, computer science, or mathematics. A minimum work experience of 4 years 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 probl