Key accountabilities/Deliverables/Outcomes

Accountability:  Business Performance Management

Insightful reporting and Data Management

• Conduct data cleaning and pre-processing tasks to ensure data accuracy and completeness.
• Develop and implement statistical models to analyze and interpret complex data sets.
• Collaborate with cross-functional teams to identify business problems and develop data-driven solutions.
• Create visualizations and dashboards to communicate insights and findings to stakeholders.
• Perform exploratory data analysis to identify trends and patterns in data.
• develop and maintain machine learning models to automate decision-making processes.
• Participate in data governance and data management initiatives to ensure data quality and security.
• Critically review output to ensure that it is a sufficient standard in terms of both presentation and accuracy.
• Continuously review processes and deliverables to find more efficient means of production and more effective means of presentation.
• Provide ad hoc analysis and management information in a timely manner.
• Production of the Daily P&L with insights.
• Communicate findings and recommendations to technical and non-technical audiences through presentations and reports.
• Stay up to date with emerging trends and technologies in data science and machine learning.

Accountability:  Post implementation reviews

• Manage the Entire PIR cycle from initial engagement with stakeholders to reporting to CMC.
• Ensure that projects are reviewed 6months post go live date.
• Assessment of projects post implementation to establish future learnings
• Collaborate with product teams to get inputs into the PIR’s.
• Facilitate clarification calls and maintain effective communication channels.
• Assign tasks set priorities to ensure effective reporting to CMC on PIRs

Role/person specification

Qualification

• Bachelor’s degree in actuarial science/data science/Math’s and Professional Qualifications
• Excellent analytical and financial modelling skills to enable financial and operational analysis and interpretation.
• Ability to assimilate, interpret and communicate complex financial analysis to non-financial people.

Experience

• Detailed knowledge of data analysis tools (Python, SQL, Excel)
• Detailed knowledge of data visualization tools (Power BI, Excel, Tableau)
• Knowledge and skills on power point presentations
• An ability to acquire skills quickly to interrogate systems,
• Good communication skills, particularly the ability to analyze and summarize large amounts of data and present in a concise and readily understood format.
• Good understanding of key banking account lines and drivers (Income, staff costs, recharges, depreciation, etc.)