Applied Machine Learning Scientist

We are looking for an experienced professional with a strong background in machine learning, including experience with time series analysis, raster processing, and/or computer vision. Geospatial and remote sensing experience are a plus. You are creative and results-driven and know how to build and evaluate models that can be effectively deployed in production. As a Machine Learning Scientist, you will apply your expertise in machine learning to build operational, scalable models that drive our commercial products, solving complex problems and integrating data from multiple sources. You will use geospatial analytics and machine learning to build operational models that are the foundation of our commercial products. You have experience building models that combine data from multiple sources, and you understand geospatial data. Expertise in machine learning is essential, but experience and knowledge of time series, multivariate statistics, and Bayesian methods are also important. You are creative and you know how to build and evaluate high quality models that can be deployed operationally. This position will report directly to the Chief Science Officer. Responsibilities Develop and implement advanced machine learning models that map ecosystem properties (land cover, carbon density, biodiversity, etc.) and changes therein Collaborate with a team of geospatial and remote sensing experts Collaborate with software engineers to operationalize models you develop in a production environment Create tools for model assessment and verification using robust statistical methods Create tools that create compelling visualizations of model results Deploy models in operational environments and support their ongoing performance evaluation and optimization Qualifications Advanced degree (preferably PhD) combined with industry experience in Computer Science, Statistics, Mathematics, is a must MS with 4+ years of relevant experience PhD with 0-2 years of relevant experience Knowledge of multivariate statistics, Bayesian methods, and time series analysis Experience with open-source programming languages (i.e. Python, R) Experience using common machine learning libraries and tools (i.e. TensorFlow, PyTorch, Scikit-Learn) Experience in computer vision, and/or deep learning Experience building and deploying production-grade machine learning systems Knowledge of geospatial analytics and remote sensing is advantageous Ability to communicate and collaborate with software engineers and product developers. Comfortable working with unstructured data and building creative solutions. Strong communication skills, positive attitude, independent and resourceful, and excited to work in a fast-paced and collaborative team environment.

Chloris Geospatial is a venture-backed technology company operating at the intersection of space-tech and nature-tech. Our mission is to accelerate the global transition to a net-zero and nature-positive economy with the most reliable, trustworthy and transparent natural capital data. Today we use industry-leading technology to measure the amount of carbon stored in terrestrial ecosystems.