Investment Impact Data Analyst

ROLE SUMMARY:
The Investment Impact Data Analyst plays an important role in supporting data-driven decision-making at aBi
Finance; ensuring that aBi management and stakeholders, including the board, have access to accurate, reliable, and
timely data analytics to inform strategic investment decisions and program design. The analyst uses advanced
statistical analysis, predictive modeling, and data visualization techniques to identify high-potential investment
opportunities, forecast investment outcomes, and assess program impact. Data is rigorously collected and
meticulously managed using robust systems and processes, with a strong emphasis on data quality assurance to
maintain the integrity and reliability of all analyses. This ensures that all decisions are based on sound, well-validated
data, improving investment performance and overall organizational effectiveness.

REPORTS TO: Investment Impact Manager
SUPERVISES: None

Qualifications
• A Master’s degree in a relevant field is highly desirable.
• Bachelor’s degree in a relevant quantitative field (e.g., Statistics, Data Science, Economics, Econometrics,
Mathematics) or a related field with emonstrated quantitative skills.
• Postgraduate diploma in M&E or Project anagement (added advantage)

Experience
• Minimum of four years of experience in a data analysis role, preferably within the finance, development, or
impact investment sector.
• Demonstrated experience in conducting data analysis, developing reports, and communicating findings to
various stakeholders.
• Experience working with large datasets and managing data quality issues.
• Experience with project management methodologies is desirable.
• Experience with data visualization tools (e.g., Tableau, Power BI) to create clear and compelling reports and
dashboards.
• Experience in developing and validating predictive models using machine learning algorithms is highly
desirable.
• Experience with specific data management and analysis tools used within aBi Finance is a plus.

Technical Skills & Knowledge
• Proficiency in statistical software packages such as R or Python, including experience with relevant statistical
modeling techniques (regression, time series analysis, etc.).
• Experience with database management systems (e.g., SQL) and data warehousing techniques.
• Understanding of data mining techniques and their application to identify patterns and relationships within
datasets.
• Familiarity with relevant data privacy and security regulations is essential.
• Understanding of development impact measurement frameworks and methodologies.