Spire Global is a 150-person space data company founded in 2012. In less than six years, Spire has built one of the world's largest satellite constellations, and we're just getting started. We are looking for a Senior Machine Learning/Data Scientist to join our Glasgow office. The Senior Machine Learning/Data Scientist will be an integral part of a small but agile team tasked with analyzing seismology data in tandem with global ionospheric total electron content (TEC) data in order to study and possibly demonstrate an earthquake precursor detection system.
The Senior Machine Learning/Data Scientist will lead a team of specialists in this potentially groundbreaking research into mitigating the human cost of catastrophic seismic activity and ultimately aiming to limit the loss of life. The interpretation of these data could potentially bolster Spire as the first commercial company with the capability to enhance understanding of novel research into earthquake precursor detection.
Responsibilities of your role:
Provide leadership and scientific knowledge to drive project activity.
Coordinate efforts of the team project deliverables and driving each milestone to successful completion.
Develops and programs integrated software algorithms to structure, analyze and leverage data in product and systems applications in both structured and unstructured environments.
Develops and communicates descriptive, diagnostic, predictive and prescriptive insights/algorithms.
Uses machine language and statistical modelling techniques such as decision trees, logistic regression, Bayesian analysis, and others to develop and evaluate algorithms to improve product/system performance, quality, data management, and accuracy.
In both theoretical development environments and specific product design, implementation, and improvement environments use current programming language and technologies to translate algorithms and technical specifications into code.
Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations.
Can work with large-scale computing frameworks, data analysis systems, and modelling environments.
Qualifications / Experience:
Applicants must have either an MSc or Ph.D. degree in STEM or associated qualification.
Exceptionally well-organized, with the ability to go as deep as needed in any project to identify and resolve problems.
Adaptability to new ideas and the ability to push the envelope of GNSS capabilities to deliver unique applications in Earth observations and predictive services.
Excellent written and verbal communication skills.
Demonstrated working experience applying machine learning techniques and advanced data science techniques to problems, preferably in the field of geosciences.
Experience and aptitude in developing hypotheses and machine learning solutions for real-world problems.
Experience developing potentially scalable systems in an industry or research environment.
Quantitative background in Statistics, Computer Science, Math or other technical fields.
Experience with common analysis tools available in Python (such as NumPy and scikit-learn) and deep learning libraries (such as TensorFlow and PyTorch).
Strong candidates will also possess skills in one or more of the following areas:
Application of machine learning techniques to geoscience data.
Knowledge and research experience in ionospheric disturbances in relation to seismic events.
Understanding of research background and techniques in processing seismic activity and TEC data.
Familiarity with or participation in the ESA SWARM Earthquake study.
Experience of using satellite data in modelling techniques.