Principal/Senior Data Scientist | Portugal

Descrição do trabalho

The Principal Data Scientist role combines scientific leadership with hands-on research, integrating programming, statistical modeling, data visualization, and ecological interpretation.

He/she leads core data science initiatives, mentors and manages data analysts, oversees data sampling strategies, and ensures the highest scientific rigor in methodologies and reporting.

His/her work lies at the intersection of ecology, statistics, and technology, with a strong focus on understanding bird and bat interactions with wind farms.

He/she employs 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 — to assess and predict wildlife patterns across onshore and offshore projects.

He/she also plays 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
• Reporting:

• Reports directly to the Director of the Biodiversity Department.
• 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.
• 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.
• 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.
• 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.
• Scientific Dissemination:

• Contributes to scientific dissemination through peer-reviewed publications, conference participation, academic collaborations, and knowledge-sharing initiatives that strengthen STRIX's scientific reputation.
• 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.
• 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.

• Qualifications
• Education:

• PhD in Ecological Statistics, Data Science, or a related field, or equivalent professional experience (6+ years) in a relevant discipline.
• Experience:

Extensive experience in core data science activities, including sampling design, power analysis, statistical analysis, data visualization, and reporting.

Proficient in ecological modeling using the R programming language and GIS tools.

Hands-on experience with bird and bat data modeling across different wind farm operational stages (onshore and offshore) is highly desirable.

• Relevant methods include radar monitoring, ESAS surveys, post-mortality surveys, viewpoint data, GIS movement data, point counts, citizen science data, species distribution mapping, and multiple collision risk modeling frameworks (including stochastic CRMs).
• Technical Skills:

Proven research and scientific reporting experience, collaborating effectively with biodiversity experts and data scientists engaged in advanced statistical modeling, species distribution modeling, distance sampling, population viability analysis, and acoustic propagation modeling.

Proficient in preparing and analyzing data from visual, video, and acoustic surveys, as well as environmental datasets from satellite imagery and predictive models.

Strong technical writing skills, exceptional at