Debt Collection Process Lead

About the role

This position analyzes and improves how the team manages outstanding payments. The work supports the Head of Debt Collection and directly affects recovery results.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Identified gaps in collections management are addressed by designing new processes and implementing improvements.

Performance understanding and results tracking for collections are supported by comfort with data and dashboards.

Analysis impact on collection outcomes is measured through strong Excel skills.

Responsibilities tied to this position are fulfilled through willingness to relocate to Nairobi, Kenya.

Requirements

The posting states a bachelor's degree requirement. Structuring and executing changes in the collections workflow requires experience in improving operational processes.

Reviewing performance metrics requires comfort using data and dashboards, along with strong Excel skills.

Coordinating initiatives requires the ability to work across Customer Service, Credit/Risk, Product/IT, Legal, and external partners.

Handling collections responsibilities requires a strong sense of ownership, with good communication and stakeholder management skills.

Performing the duties of this role requires willingness to relocate to Nairobi, Kenya.

Nice to have

Applying relevant best practices and standards is supported by debt collection process experience.

Interest in automation and practical tech improvements, such as rules-based case allocation, workflow tools, customer communications, and AI, is preferred.

Supporting reporting and analysis for collections work is preferred through experience with Power BI, SQL, or similar tools.

Practical notes

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data roles turn raw information into decisions that shape business outcomes. Analysts, data scientists, and data engineers rely on statistics, coding, and communication to work with business teams. Modern companies depend on data teams, and a strong portfolio of analyses often matters more than degrees. Tools like dashboards, SQL, and visualization platforms help professionals turn findings into action. Continuous learning is necessary because the field evolves quickly.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

Greydale Africa http://www. greydaleafrica. com/ is an integrated B2B marketing and social impact agency for organizations in the technology sector.