Data Scientist Remote Sensing

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United Kingdom

Job Type

Full-Time

About the Role

Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline;
Perform independently statistical analysis, computer programming, predictive modeling and experimental design;
Create innovative insights from imagery and sensor data with a focus on large scale geo-temporal analyses, computer vision and remote sensing, feature extraction from imagery and time series data, crafting complex model architectures using embeddings and ML/DL techniques;
Build cross-functional relationships to collaboratively partner with the business and effectively network within the Data Science Community;
Use advanced mathematical models, machine learning algorithms, operations research techniques, and strong business acumen to deliver insight, recommendations and solutions;
Develop sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact and key performance indicators;
Present compelling, validated stories to all levels of organisation, including peers, senior management, and internal customers to drive both strategic and operational changes in business.

Requirements

  • MSc Remote Sensing and Environmental Mapping or Earth Observation & Geoinformation.

  • Knowledge of ENVI software and MATLAB is essential.

  • Knowledge of GEE and Google API Developing. 

  • Educational preparation or applied experience in at least one of the following areas: Machine Learning, Electrical/Industrial Engineering, Operation Research, Biostatistics, Computational Biology, Applied Mathematics, Computer Science, Geographic Information Systems and/or other related quantitative discipline;

  • At least one year of experience with R, Python, Java, Scala, and/or C/C++ (R and Python strongly preferred);

  • Demonstrated intermediate proficiency in computational skills and level of experience building data models using R, Python or other statistical and/or mathematical programming packages, including computer vision algorithms and libraries;

  • Demonstrated basic understanding of software development best practices (including Version Control, Code Documentation & Review, Cloud Based Sequence Analysis, Database Management);

  • Strong proficiency in predictive modeling to include comprehension of theory, modeling/identification strategies and limitations and pitfalls;

  • Strong proficiency with geospatial and imagery data such as geophysical soil sensing, remote sensing, hyperspectral, multispectral imagery, open source geospatial technologies and large-scale cloud computing;

  • Experience in successful delivery of valuable analysis through application of domain knowledge; evidence of ability to strong business acumen;

  • Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise and actionable manner to extended team and small groups of key stakeholders.

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