Portfolio Data & Results Consultant

The Portfolio Data & Results Consultant will serve as a critical technical contributor to DMEL's data and reporting deliverables, leveraging advanced analytics and Python-based development to extract, structure, and analyse data to support portfolio analytics and reporting. The role supports the implementation of key objectives in the 2025–2027 DMEL Work Programme and reports to the Monitoring, Results and Data Management Manager.

Responsibilities

• Develop and maintain Python-based tools to extract and structure data from non-standard APR Excel templates, automating disbursement and financing breakdown extraction.
• Build NLP-driven Python models to extract thematic project and other data from unstructured PDFs, enabling enhanced portfolio, COP and contributor reports and analyses.
• Contribute to Python-supported data cleaning, migration, and integration pipelines (ETL) across PPMS/iPMS, underpinning analytics and reporting workflows.
• Prepare visualizations and analytical summaries for internal reports, Board materials and external communications, including UNFCCC COP reporting.
• Deliver analyses on USP2/USP3 indicators, including development and clarification of methodologies.
• Validate and rectify portfolio data in iPMS to address tagging and consistency issues.
• Maintain dashboard-ready datasets underpinning portfolio-level reporting.
• Flag data issues affecting trend analysis and portfolio interpretation.
• Support regional and thematic portfolio analyses through data-driven presentations and technical follow-up on portfolio composition and performance.
• Maintain and enhance Power BI dashboards and supporting datasets tracking implementation, disbursement, and climate results across the portfolio.
• Provide technical inputs to the design and refinement of interactive dashboards, including enhancements to semantic models, data filters, and alignment with GCF results frameworks.
• Coordinate with regional teams and project leads to validate dashboard content and improve usability.
• Prepare and refine datasets for contributor reporting.
• Support clarification of assumptions and data limitations for external sharing and stakeholder engagement.
• Extract, clean, and analyse ex-post co-financing data from APRs; document methodological and system constraints; prepare project-level and aggregate datasets.
• Identify and flag data inconsistencies across monitoring platforms; contribute to the application of validation rules and cleaning protocols.
• Coordinate with DIT and OCIO on system limitations and interim solutions pending full GPP–iPMS integration.
• Support development and documentation of SOPs and data dictionaries to standardize data entry and reporting practices.
• Coordinate DMEL inputs to internal data requests.
• Assist in preparing training materials, user guides, and FAQs related to data systems and results reporting tools.
• Support team briefings, data clinics, and internal learning sessions to disseminate findings and promote capacity building.
• Contribute to updates to the RPSP knowledge site and other internal knowledge repositories.
• Undertake background research in support of results framework harmonization (RMF, IRMF, RRMF).
• Carry out additional tasks as assigned by the supervisor.

Requirements

• Bachelor's degree in Applied Mathematics, Data Science, Statistics, Computer Science, Economics, or a related quantitative field.
• A Master's degree is a plus.
• Minimum of 1–2 years of hands-on experience in data analytics, data engineering, or monitoring and evaluation in a climate finance, development finance, or international organization context.
• Demonstrated experience developing Python-based data extraction, automation, and analysis tools, including work with semi-structured and unstructured data sources (e.g., Excel, PDF, APR templates).
• Proven experience with NLP or machine learning methods applied to classification or text analysis tasks.
• Experience with ETL pipeline development and database integration (e.g., PPMS/IPMS or comparable systems).
• Track record of contributing to high-stakes institutional reporting & analysis.
• Advanced proficiency in Python (required).
• Advanced proficiency in Power BI, including DAX and semantic model development (required).
• Proficiency in SQL and advanced Excel.
• Familiarity with R, MATLAB, or C/C++ is an asset.
• Experience with statistical methods including regression analysis, GLM, GAM, clustering, and NLP.
• Strong analytical, problem-solving, and organizational skills with demonstrated ability to manage multiple workstreams simultaneously.
• Excellent verbal and written communication skills in English; ability to translate complex technical findings for non-technical audiences.
• Demonstrated ability to work independently and collaboratively in multicultural, cross-functional teams.
• Capacity to serve as a coordination point across internal teams (e.g., DMEL, DIT, OCIO) and external stakeholders.

Skills

• Data Analytics
• Data Engineering
• Monitoring and Evaluation
• Python Development
• Data Extraction Automation
• Natural Language Processing
• Machine Learning
• ETL pipeline design
• Database Integration
• Institutional Reporting
• Power BI
• DAX
• Semantic Model Development
• SQL
• Advanced Microsoft Excel
• Regression Analysis
• Generalized Linear Models
• Generalized Additive Models
• Clustering
• Statistical Methods
• Text Analysis

Languages

English