Ecological Data Scientist
The role of the Ecological Data Scientist combines scientific leadership with hands-on research, integrating programming, statistical modeling, data visualization, and ecological interpretation to drive environmental sustainability in the renewable energy sector.
The successful candidate will lead core data science initiatives, mentor and manage data analysts, oversee data sampling strategies, and ensure the highest scientific rigor in methodologies and reporting.
This position requires a strong focus on understanding bird and bat interactions with wind farms, employing a broad suite of analytical techniques including radar and camera monitoring, ESAS surveys, viewpoint observations, GIS-based movement analysis, post-mortality surveys, citizen science data, and stochastic collision risk modeling.
The successful candidate will play a central role in advancing bat research, developing models of bat activity and behavior to guide curtailment measures and inform effective mitigation strategies.
Key Responsibilities:
1. Data Strategy and Study Design:
* Develops and oversees data collection strategies for radar, camera, acoustic, and viewpoint surveys.
* Designs studies and experiments to ensure efficiency, representativeness, and data quality, while integrating uncertainty estimates into models and reports to ensure transparent communication of analytic limitations.
2. Statistical Analysis and Modelling:
* Applies advanced statistical inference and modeling techniques to analyze biodiversity and ecological datasets in alignment with project objectives.
* Develops and validates collision risk models to predict avian collision probabilities using multiple data sources, ensuring model accuracy and reliability through comparison with observed data.
3. Data Management and Quality Assurance:
* Manages and maintains data and metadata systems to ensure integrity, organization, accessibility, and comprehensive documentation.
* Identifies, quantifies, and mitigates data biases in radar and other sensor-derived datasets through calibration, validation, and methodological refinement.
4. Workflow Automation and Reporting:
* Automates analytical workflows to enhance operational efficiency, reproducibility, and scalability of data analysis processes.
* Prepares comprehensive technical reports detailing methodologies, model outputs, and scientific findings for diverse stakeholders.
5. Scientific Dissemination:
* Contributes to scientific dissemination through peer-reviewed publications, conference participation, academic collaborations, and knowledge-sharing initiatives that strengthen STRIX's scientific reputation.
6. Interdepartmental Support:
* Supports business development by ensuring the scientific and technical quality of project proposals and providing expert input during proposal preparation.
* Contributes to technological and analytical software development, and trains and supervises junior data analysts.
7. Continuous Improvement:
* Reviews and analyzes lessons learned from each project, contributing to the effectiveness of the management system through process optimization, quality control, and continuous improvement to ensure customer satisfaction.