Project Management Support Analyst - Data Analyst

Under the direct supervision of the TAMU Project Coordinator and with support of the Technical Specialists, the Project Management Support Analyst is expected to support in

1. Data management: Data Collection & Cleaning, Data Analysis, visualization & Interpretation

2. Data Modeling & Machine Learning

3. Big Data Processing & Integration

4. Insight Generation & Strategic Decision Support

5. M&E, Knowledge Management, Innovation and capacity building 

• Data collection, structuring, quality management, analysis and presentation, and their intersection with policies, processes and project implementation

• Work with Project Managers to identify data analysis and visualization needs to develop tailored processes and tools to address the  data quality and integrity needs

• Design, conduct, document and present analyses on complex and connected data, proposing and testing hypotheses, deriving statistics, trends and insights, and proposing improvements to the project manager for incorporation in projects and processes

• Conduct rigorous data quality analysis and testing of data sources and tools

• Support the development of automated and interoperable information management systems and dashboards to enable the translation of raw data into interactive visual platforms to track real-time progress and inform decision making. 

• Maintain and update the MIS platforms to ensure that PM data are readily available.

• Compile and analyze data to support the PMs in the preparation of monthly, quarterly, and annual reports as well as ad hoc updates which provide comprehensive insight to the progress, constraints, gaps, and lessons of the project both to internal and external stakeholders.

• Conducts and shares project performance analysis and supports Program Management Office and Leadership team in making informed decisions

• Support the Project Teams in preparing briefing materials on project performance and status, as required.

• Other related technical and analysis support as required with support of the Data Specialist

• Provide support to develop predictive models with measurable accuracy and performance metrics.

• Support the Implementation of automated algorithms that adapt and improve with new data.

• Generate simulation and forecasting outputs to guide planning and decision-making.

• Build optimized, scalable data pipelines capable of handling large datasets.

• Integrate clean, unified datasets from diverse structured and unstructured sources.

• Enable real-time or near-real-time data processing capabilities

• Develop concise, audience-appropriate reports and dashboards delivered on time.

• Data-driven reports for operational, or business improvements.

• Generate Key insights that the PMs can translate into practical recommendations for decision-makers.

• Produce Data stories that connect numbers to real-world impacts.

• Support the compilation, consolidation and analysis of relevant data related to the portfolio, Programme and Project(s);

• Support the design, development, testing, implementation and documentation of data collection and reporting system, processes and tools, including dashboards and reports;

• Support the gathering and documentation of user requirements and expectations, reporting requirements, design preferences, and quality criteria;

• Provide substantive inputs to assist the PMO and Project Teams in preparing project progress and completion reports, project briefs and any communication materials as needed;

• Contribute to maintaining records of project files as needed;

• Support knowledge management initiatives to ensure that project experiences, best practices and learning are documented and available to all UNOPS ETMCO Projects and Programmes;

• Provision of training/ orientation to project teams to ensure tools and data use is to their full potential

• Support the review data management practices across country offices and propose process improvement initiatives

• Participate in relevant Communities of Practice;

• Support project teams in identifying, capturing and disseminating lessons learnt.

• Contribute in developing data management guidance and SOPs and provide related training.