Jobs at Credit Direct Limited – Apply Now

The Data Analyst (Graduate Intern) will support the Financial Inclusion sales team by managing customer data, maintaining an organized leads database, and providing basic analytical insights to support sales growth. The role is designed for a recent graduate with strong data skills who will be trained to support customer onboarding, sales tracking, and lead management across the Credit Direct ecosystem.

Job Details

Customer Data Management

• Create and maintain customer profiles for all customers onboarded by the Financial Inclusion sales team.
• Ensure accurate and timely data entry for new customers and sales transactions.
• Review and clean customer data to eliminate duplicates and inconsistencies.

Leads Database Management

• Maintain and update the department’s leads database.
• Consolidate leads generated from multiple channels across the Credit Direct ecosystem.
• Organize leads into structured categories to support sales conversion.

Sales Tracking & Reporting

• Track sales activity and customer onboarding across Relationship Officers.
• Maintain records of leads, conversions, and loan disbursements.
• Produce weekly reports on sales pipeline and lead conversion performance.

Data Analysis & Insights

• Analyze leads and sales data to identify patterns and opportunities for improved conversion.
• Support the team with simple dashboards and performance summaries.

Marketing Collaboration

• Work with the marketing team to ensure continuous engagement of leads through campaigns and targeted communication.
• Provide updated leads lists for marketing outreach and track engagement outcomes.

Requirements

• Bachelor’s degree in Economics, Statistics, Mathematics, Computer Science, Business Administration, or a related field.
• Less than 1 year of work experience (recent graduates strongly encouraged to apply).
• Strong interest in data management, analytics, and sales operations.

Skills & Competencies

• Strong proficiency in Microsoft Excel and data handling.
• High attention to detail and accuracy in data entry.
• Basic data analysis and reporting capability.
• Ability to organize large datasets into structured formats.
• Strong problem-solving and analytical thinking.
• Ability to work in a fast-paced, sales-driven environment.

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Design, build, and deploy predictive models that improve customer segmentation, risk assessment, and credit decisions. Develop risk modelling and stress testing frameworks. Drive data-driven strategies that enable personalized offerings, boost portfolio performance, and support smarter, faster, and more responsible lending practices.

Data Modelling & Customer Profiling:

• Build and maintain predictive models for customer segmentation and risk assessment. 
• Develop clustering techniques to better understand customer behaviour patterns.

Credit Risk Analytics:

• Create scoring models for default risk, fraud detection, and creditworthiness evaluation.
• Develop risk modelling and stress testing frameworks.

Model Deployment & Monitoring:

• Work with tech teams to deploy models into production systems.
• Monitor model performance and adjust when needed.

Stakeholder Engagement & Communication:

• Build relationships with internal stakeholders, customers, and external partners.

Compliance & Data Governance:

• Ensure models meet internal policies and regulatory requirements.
• Maintain data accuracy, ethics, and security standard.

Requirements

• B.Sc./M.Sc. in Data Science, Statistics, Computer Science, or related field.
• Professional certifications in data analytics or machine learning (preferred).
• Minimum 4 years in data science or risk analytics roles.
• Experience in financial services, particularly lending, is strongly preferred.

Technical:

• Predictive modelling & machine learning.
• Credit risk analysis & scoring.
• SQL, Python.
• Risk modelling and stress testing.
• Experience with cloud platforms (AWS, Azure).

Behavioral:

• Analytical and curious mindset.
• Effective communication and storytelling skills.
• Strong stakeholder management.
• Team collaboration.
• Accountability and ownership.
• Continuous learning & improvement mindset.

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