Data Scientist, P4

Education

• Advanced university degree (Master’s degree or equivalent) in data science, computer science, artificial intelligence, or a closely related field, is required. A first-level university degree in combination with qualifying experience may be accepted in lieu of the advanced university degree.
  

Work Experience

• A minimum of (7) seven years of progressively responsible experience in data science is required. Experience in using data to advance decisions, strategies and execution is required. Experience managing small teams is required.
  
• Experience of working with international multi-agency projects, ideally in an environmental context is desirable.
  
• Experience of data science tools such as Jupyter, Matlab, Knime, SPSS, SAS, or similar Statistical Programming Languages such as R, Python, Javascript or related is desirable.
  
• Experience contributing to technical publications (peer- reviewed or on established technical fora) in AI or data science is desirable Experience building AI/data models for the environment is desirable.
  

• Studies, explores, and evaluates new and existing data sources to determine their effectiveness and accuracy for use in decision making, and assess their suitability for actionable output. Provide data-driven recommendations to stakeholders.
  
• Develops, implements and evaluates assigned programmes, monitors and analyzes their development and implementation, and drafts and reviews relevant documents and reports.
  
• Designs and builds predictive data science products, such as visualizations, models or AI/ML algorithms for initial simulations or prototypes and subsequent ingestion into mainstream software applications.
  
• Liaises with technology stakeholders to access infrastructure, software and services needed to develop and deploy data science products.
  
• Promotes use of data science solutions by showcasing the products to stakeholders; disseminating of data products, and raising awareness of available data products.
  
• Develops products, tools and processes to periodically validate the effectiveness of data science products and their expected and observed benefits. Assesses impact on business defined key performance indicators; Iteratively improves products based on findings.
  
• Engages in complex research and analyses to develop data products; uses internal and publicly available data and information.
  
• Oversees and allocates resources to the building of analysis, reporting and quality control capabilities, contributes to short- and long-term planning of UNEP and assists in identifying threats and opportunities to the successful implementation of its mandate.
  
• Coordinates with different functional teams to implement models and monitor outcomes and translate organizational needs into analytics and reporting requirements to support decisions, strategies and workflows with data and information.
  
• Develops processes and tools to monitor and analyze model performance and data accuracy and conduct full data analytics lifecycle analysis, including data requirements, activities and design.