Product Manager - Retention Job at M-KOPA

M-KOPA

Product Manager - Retention Job Post

Location: Jobs in Uganda 2025 - 2026

Work Hours: Full-time, 08 hours per day

Salary: UGX

No. of vacancies: 01

Deadline: January 30 2025

Hiring Organization: M-KOPA

Job Details:

We foster a low-ego environment where diversity, innovation, and collaboration drive both commercial growth and social impact. Our team values psychological safety and takes a discovery-first approach to problem solving. You'll be empowered to make data-driven decisions and clear cases for prioritization of solutions in a domain that you have a high degree of ownership over.
We are looking for a technical and/or analytical PM for this role — if you have a fondness for engineering and/or you love digging into data, you might be a good fit!

In this role, you would be responsible for:
• Owning the strategic direction and evolution of critical engineering systems and business processes
• Working with engineers to drive continuous improvement in our systems’ capabilities
• Partnering with data scientists and analysts to understand user needs and implement novel methodologies
• Getting feedback from customers and customer care teams to understand customer pain points
• Analyzing data to understand the largest areas of opportunity for further improvements

This is a fully remote role and will include travel across our different markets and working within the UTC -1 through UTC+3 time zones.

Your application should demonstrate:
• 3+ years of product management experience
• Strong technical and/or analytical skills, including the ability to conduct your own complex data analysis
• Proven ability in solving technical and/or analytical problems
• Ability to work with diverse, distributed teams across multiple countries
• Strong interpersonal, communication, and collaboration skills
• Experience in data-driven decision making and strategy development
• Experience with agile methodologies and cross-functional team leadership
• Nice to have: Experience with data science models