Product Analyst - Credit Eligibility

As a Product Analyst - Credit Eligibility at M-KOPA, you will be responsible for analyzing repayment data to improve loan eligibility criteria and developing new loan offers based on customer demand. You'll also monitor credit performance, work with engineers and data scientists to implement data-driven changes to eligibility criteria, and effectively communicate your findings to diverse teams.

To qualify for the Product Analyst - Credit Eligibility role at M-KOPA, candidates should have several years of experience in analytical roles, strong analytical skills, proficiency in SQL for data sourcing, and a proven track record of solving complex analytical problems. Effective communication skills and the ability to collaborate with distributed teams are also essential.

An ideal candidate for the Product Analyst - Credit Eligibility role at M-KOPA should possess strong analytical skills, the ability to conduct analysis on unstructured data, excellent data communication abilities, and interpersonal skills to work collaboratively within diverse teams. Familiarity with data science models would be a plus.

M-KOPA is dedicated to empowering employees through various development programs, coaching partnerships, and hands-on training. The company values individual career journeys, offering flexibility and family-friendly policies that cater to the well-being of its staff.

Yes, the Product Analyst - Credit Eligibility position at M-KOPA is fully remote. You can work from anywhere within the specified time zone (UTC -1 / UTC+3), allowing you the flexibility to collaborate with a diverse team across multiple countries.

When analyzing repayment data, I typically start by cleaning and organizing the data to identify trends or patterns. I employ statistical methods to measure performance, and I always look for anomalies that could indicate areas for improvement. Context is key, so I ensure my analysis aligns with business goals.

Developing new loan offers and pricing strategies involves thorough market research to understand customer preferences and competitive offerings. I analyze historical data to determine the optimal pricing structures and use forecasts to project demand. Collaboration with internal teams is crucial for validation and refinement.

I have extensive experience writing SQL queries to extract and manipulate large datasets. I use SQL to perform joins, aggregations, and complex queries that help me analyze loan performance and customer demographic data effectively, ensuring that my insights are data-driven.

I tackle ambiguous data problems by breaking them down into smaller, more manageable components. I use exploratory data analysis to gain insights and formulate hypotheses, and I consult with team members to gather diverse perspectives. Prototyping and testing solutions also play a significant role.

I have a rich history of collaborating with cross-functional teams, including engineering, data science, and product development. I prioritize clear communication and understanding each team’s roles to ensure that projects align with overall objectives while leveraging diverse skills to drive success.

To monitor credit performance, I track key metrics such as default rates, repayment intervals, and customer feedback. I use dashboards and regular reports to keep the team informed and facilitate data-driven discussions on necessary adjustments to eligibility criteria and loan terms.

Testing the predictive capabilities of new datasets involves defining performance metrics such as accuracy and precision. I use statistical modeling techniques to evaluate how well the dataset predicts credit performance and continually refine the model based on results from test scenarios.

In a previous role, I encountered a significant spike in default rates. I conducted a root cause analysis, which revealed that the existing eligibility criteria didn’t align with customer circumstances. By recalibrating these criteria based on new analytical models, we significantly reduced defaults.

Prioritization is key, so I assess the potential impact of analyses and recommendations on business objectives and customer satisfaction. I use a combination of data-driven insights and collaboration with stakeholders to prioritize effectively, ensuring that we focus on the most impactful areas first.

I am drawn to M-KOPA because of its commitment to innovation and social impact through financial empowerment. I admire the company’s values around collaboration and diversity, and I'm excited by the opportunity to leverage my analytical skills to drive meaningful change in the lives of customers across Africa.