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The research will advance the use of multiple systems estimations in humanitarian and public health contexts by developing reproducible, multilingual workflows for social media analysis, building data pipelines in R/Python, and creating open-source tools for text and feature extraction. They will also assess and mitigate biases in social media data and design evidence-based heuristics to guide researchers in applying these methods effectively.
Job Responsibility:
Advance the use of multiple systems estimations in humanitarian and public health contexts
Develop reproducible, multilingual workflows for social media analysis
Build data pipelines in R/Python
Create open-source tools for text and feature extraction
Assess and mitigate biases in social media data
Design evidence-based heuristics to guide researchers in applying these methods effectively
Requirements:
Postgraduate degree, ideally a doctoral degree, in a relevant topic
Proven expertise in data science or related fields
Strong skills in quantitative analysis applied to large or complex datasets
Knowledge of social media data sources, including accessibility, platform characteristics, ethical considerations
Experience developing or applying search strategies and lexicons
Proficiency in fitting and validating statistical models or machine learning algorithms
Advanced skills in R and/or Python for data processing and analysis
Familiarity with natural language processing techniques, such as text pre-processing (tokenization, stemming/lemmatization) and feature extraction
What we offer:
Annual leave entitlement is 30 working days per year, pro rata for part-time staff