Associate Evaluation Data Scientist

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Description

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• Develop effective data management practices in evaluation. This includes creating easy-to-use templates for data processing and ensuring consistent data quality.
• Create clear, repeatable workflows for data cleaning, analysis, and visualization, tailored to specific evaluation needs.

• Provide comprehensive guidance to the evaluation teams, enhancing their capabilities in understanding, accessing, cleaning, analysing, and visualizing data related to EIB Group activities.
• Where required, work with consultants, ensuring effective collaboration and high-quality outputs in data analysis projects.
• Develop up-to-date knowledge of potentially available sources of (internal and external) data relevant to the IG/-/EV’s work programme, and proactively advise evaluation teams on these data sources, potential Data Science methods and AI tools for individual evaluations.
• Cooperate with a wide range of stakeholders within the EIB Group, including the evaluation teams, data owners across the EIB Group, and the Group’s IT services.

• Drive innovation in qualitative data analysis within IG/-/EV by exploring and integrating a wide array of data science techniques and AI tools, including but not limited to Large Language Models (LLMs), text mining, qualitative data coding.
• Actively contribute to shaping IG/-/EV’s long-term vision for quantitative data analysis, ensuring that evaluation methods remain cutting-edge and effective. This also includes participating in other relevant activities within IG/-/EV to continuously advance the division’s analytical capabilities.

• University degree (minimum an equivalent to a Bachelor) preferably in Data Science, Finance, Statistics, Economics, Econometrics, Mathematics or other closely relevant field.
• Minimum 3 years of relevant professional experience, preferably in academia, consultancy, economic research, banking, or finance.
• Prior proven experience in quantitative analysis, including the cleaning, tidying, visualization and statistical analysis of granular data (e.g., large-scale firm-level datasets, financial transaction records, or similar.) Hands-on experience with statistical methods for causal analysis would be considered a strong asset.
• Proven experience with advanced text data analysis methods, such as text mining, sentiment analysis, key feature extraction, text frequency analysis, and qualitative data coding will be considered a significant asset. Knowledge of LLM will be considered an asset.
• Ability to think conceptually, developing innovative and creative ways to support evaluations with quantitative data, using a multitude of analytical tools (e.g., statistical, and econometric methods, machine learning, geospatial analysis etc).
• Solid work experience in using Microsoft Office tools, particularly Excel.
• Proven familiarity with the data analysis and visualization tools in Python and GitHub. Experience with STATA, R and/or MATLAB is considered as an advantage.
• Ability to design complex queries from data warehouses (e.g., SQL, Business Objects).
• Strong communication and stakeholder management skills, adept at translating and presenting complex data and technical concepts to various audiences.
• Excellent knowledge of English and/or French with a good command of the other. Knowledge of other EU languages would be an advantage.

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