LAPO Microfinance Bank is a pro-poor financial institution committed to the social and economic empowerment of low-income households through provision of access to responsive financial services on a sustainable basis. The Institution was established in the late 1980s as a Non-Governmental Organization (NGO) by Godwin Ehigiamusoe in response to the effects of the implementation of the Structural Adjustment Programme (SAP) in 1986. In 2010, LAPO MfB obtained the approval of the Central Bank of Nigeria (CBN) to operate as a state microfinance bank and in 2012, it got an approval as a national microfinance bank. Over the years, LAPO MfB has emerged as a leading institution delivering a range of financial services to over a million people in Nigeria. Our Vision/Mission: Improving lives The LAPO MfB family share mutual core values that are centred on: Integrity Innovativeness Simplicity Excellence Customer-centeredness
Key Responsibilities:
- Data Collection & Preparation:
- Gather data from various sources (e.g., databases, CRM systems, web analytics, market research).
- Clean and preprocess data to ensure accuracy and consistency.
- Transform data into usable formats for analysis.
- Data Analysis & Interpretation:
- Conduct data analysis using statistical methods and tools.
- Identify trends, patterns, and anomalies in data.
- Interpret data and draw meaningful conclusions.
- Develop data visualizations (e.g., charts, graphs, dashboards) to communicate findings effectively.
- Reporting & Communication:
- Prepare reports and presentations summarizing data analysis results.
- Communicate findings to stakeholders in a clear and concise manner.
- Answer ad-hoc data requests from business users.
- Data Modeling & Visualization:
- Develop data models to represent business processes and data relationships.
- Create interactive dashboards and visualizations to provide insights into key metrics.
- Collaboration & Collaboration:
- Collaborate with business users to understand their data needs and provide analytical support.
- Work closely with IT teams to ensure data quality and availability.
- Data Quality Management:
- Implement data quality checks and procedures.
- Identify and resolve data quality issues.
- Tool & Technology Proficiency:
- Utilize data analytics tools and technologies (e.g., SQL, R, Python, Tableau, Power BI). Specify the tools used by the organization.
- Stay up-to-date with new data analytics tools and techniques.
Method of Application
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