Basic Function:

• The Senior TQLA Specialist Data Visualization and Utilization will promote dialogue using finance data to make decisions, take corrective actions and inform performance improvement by establishing platforms for situation room meetings (SRM) to review the financial health of the project. Will support provision of technical assistance aimed at strengthening collection, analysis, and use of data for decision making.
• This position holder will ensure participants at each SRM shall include project managers and other actors from NASCP and SASCPs, to conduct peer reviews within and between states and ensure financial information shall be collected at granular level, collated, analyzed, visualized, presented, and discussed. Promote transparency and accountability in financial information for greater beneficiary experience, program impact and societal benefits T
• he Senior TQLA Specialist Data Visualization & Utilization will supervise and utilize large data sets to find opportunities for improving program implementation, strategy optimization, and providing data-driven feedback to both NASCP and FHI 360.

Duties and responsibilities:

• Promote dialogue using finance data to make decisions, take corrective actions and inform performance improvement by establishing platforms for situation room meetings (SRM) to review the financial health of the project
• Support NASCP and SASCP to comply with applicable statutory, regulatory, and other project requirements, including adopting and using an effective compliance program.
• Promote transparency and accountability in financial information for greater beneficiary experience, program impact and societal benefits
• Assist NASCP to prepare funding and fund utilization dashboards, disaggregated by states. The dashboard shall highlight cashflow and program outcomes (e.g., US dollars spent to identify each HIV positive pregnant women in X state), disaggregated by state contributions versus GF funding over time.
• Carry out spot-checks at national (NASCP) and sub-national (state, LGA, health facility, community) levels to review the value chain and integrity of financial data being reported. This will range from data collection, collation, reporting, analysis, and data utilization for decision-making.
• Track to verify that appropriate financial data are duly recorded with the right supporting documentations. That the right source documentations are being used for data collection, collation, analysis, presentation, and reporting. Where quality gaps are noted, MOH teams will be supported to follow appropriate protocols and processes for improvement. Standard data collection tools will be put into effective use, to support granular level data collection and analysis, appropriate to identify and address any data quality gaps on just-in-time (JIT) bases.
• Review data visualization dashboards to highlight data points needing greater accuracy, completeness, consistency, and validity, and make any findings accessible, readable, and appreciable by applicable program managers and frontline workers.
• Provide hands-on training and mentoring support to the finance and program staff on financial management, financial recording, and reporting, pipeline analysis, burn rate monitoring, compliance with donor policies and procedures and prevention of fraud, waste, and abuse.
• Conduct periodic spot checks to verify data reported during financial surges due to increases in program activities (e.g., trainings, supportive supervisory visits, etc.).
• Disseminate dashboards consisting of validated information, to generate dialogue, promote evidence-based decision making, address corrective actions and foster performance improvement in financial management.
• Use peer review approach, generate monthly dashboards highlighting results and best practices in financial performance across states, to catalyze action among lagging state actors ensuring that key best practices identified during regular SRMs, or field-based practices shall be shared across the project as knowledge management and thought leadership products.
• Develop data analysis plans and prepare reports based on data analysis plans
• Work with stakeholders and the organization to identify opportunities for leveraging innovative technology/statistical models for business development.
• Mine and analyze data from company databases to drive optimization and improvement of implementation strategies.
• Advise and assist in the development of inferences and conclusions, as appropriate

Knowledge, Skills & Attributes:

• Report to supervisor on variances and status on regular basis. Perform detail-oriented work with a high level of accuracy. Interact with diplomacy and tact and follow-up on requests in timely and efficient manner.
• Strong problem-solving skills with an emphasis on innovation design and strategic frameworks using technology.
• Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• A drive to learn and master new technologies and techniques. Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, etc.
• Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
• Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
• Excellent written and verbal communication skills in English for coordinating across teams.

Qualifications and Requirements:

• BSc in Computer Science, Computer Engineering, Health Information Management, Statistics, Mathematics or another quantitative field with relevant Professional Certification in Information and Communications, Technology, and related disciplines. 9 to 11 years relevant experience manipulating data sets and building statistical models.
• MSc in Computer Science, Computer Engineering, Health Information Management, Statistics, Mathematics or another quantitative field with relevant Professional Certification in Information and Communications, Technology, and related disciplines. 7 to 9 years relevant experience manipulating data sets and building statistical models
• Experience using statistical computer languages (R, Python, SLQ, STATA, SAS, etc.) to manipulate data, querying databases, and draw insights from large data sets.
• Experience working with and creating data architectures.