The Data Analyst – Product & Credit reports to the Senior Data Analyst and works daily across both the product and credit/risk teams. You will partner closely with the Analytics Engineering Lead on technical foundations while owning the analytical insight layer that informs strategy on both sides of the business. This is a solo contributor role with high autonomy, requiring the ability to build from ambiguity and communicate findings to non-technical stakeholders clearly.
Primary Work Focus
Enablement: Building self-service BI tools, dashboards, and automated reporting so that product, commercial, and credit stakeholders have immediate access to performance data without analyst bottlenecks.
Deep analysis: Owning causal and attribution investigations that correlate complex variables — funnel behaviour, promo cycles, device performance, payment patterns — to explain the true drivers of business outcomes.
Key Responsibilities
Product Analytics
Design and maintain dashboards covering acquisition funnels, conversion rates, channel performance, and customer segmentation across online and retail.
Analyse the impact of promotional campaigns, pricing changes, and device mix decisions on conversion, revenue, and customer lifetime value.
Partner with commercial and marketing stakeholders to design experiments (A/B tests, holdout groups) and deliver attribution analysis that separates signal from noise.
Identify behavioural patterns in the customer journey that inform product development, UX decisions, and go-to-market strategy.
Credit and Portfolio Analytics
Build and maintain self-service dashboards for portfolio health, delinquency trends, collections performance, and risk segmentation.
Conduct causal investigations correlating device performance, customer demographics, promotional history, and payment behaviour to identify true default drivers.
Support underwriting optimisation through data-driven analysis of approval criteria, score thresholds, and policy change outcomes.
Translate credit performance data into clear, actionable recommendations for the credit and risk leadership team.
BI Infrastructure and Data Quality
Design and automate data pipelines that transform raw credit bureau data, payment histories, and operational systems into reliable, production-grade datasets.
Partner with the Analytics Engineering Lead to establish data quality standards, documentation practices, and analytical frameworks that enable progressive data autonomy across teams.
Reduce dependency on ad-hoc analyst requests by building repeatable, self-service data products.
Requirements and Qualifications
Education: BA/BSc/HND qualification in a relevant field.
Experience: 3–5 years of experience in an analytics or BI role, with a track record of building self-service dashboards and automated reporting.
SQL Mastery: Advanced SQL and data transformation expertise, including dimensional modelling, ETL/ELT pipelines, and production-grade dataset design.
Statistical Knowledge: Solid grounding in statistical methods (A/B test design, regression analysis) and the ability to distinguish correlation from causation in messy business data.
Communication: Demonstrated ability to communicate analytical findings to non-technical stakeholders in writing and in person.
Interested and qualified candidates should apply via the official MoPhones Talentlyft portal. You can access the application link here: MoPhones Application Portal.
