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.