Full-Time Data Scientist, Computer Vision

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 transform remote sensing data, including multi- and hyper-spectral, into actionable insights that guide our exploration programs. You will further develop KoBold’s in-house remote sensing instrumentation and develop modeling tools to optimize further data collection efforts. You will be creating insights that 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 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 analysis techniques to solve scientific problems. 

Responsibilities

The Computer Vision Scientist will:

• Apply state-of-the-art analysis techniques, including extracting features and classes from gigapixel scale multispectral images. 
• Model and simulate the imaging chain from sensor through optics and processing to improve data collection and guide development of future instruments
• Help develop KoBold’s proprietary software exploration tools.
• 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, including both reflectance and emissivity spectroscopy, 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 Computer Vision Scientist candidate will have:

• A successful track record of working with imaging instrumentation, especially scientific cameras and illumination systems as well as developing and deploying state-of-the-art spectral analysis techniques. Bonus for multispectral/hyperspectral imaging systems and reflectance and emissivity spectroscopy. 

Technical skills, including extensive experience with:

• Physical measurement and data analysis systems. 
• 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.
• Using cloud computing resources to perform scientific analysis at scale
• 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:

• Traditional image processing techniques such as edge detection, transforms, image stitching, etc… 
• Deep learning image processing techniques such as supervised and unsupervised classification.
• Broad skills in and knowledge of data analysis, physics, and applied statistics.

Training and work experience:

• An advanced degree in the physical sciences, engineering, computer science, or mathematics or equivalent industry experience.
• Post-degree work experience as a scientist or engineer.

Work practices and motivation:

• Ability to take ownership and responsibility of large projects.
• 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.
• 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.