About

Equity Bank

is one of the region’s leading Banks whose purpose is to transform the lives and livelihoods of the people of Africa socially and economically by availing them modern, inclusive financial services that maximize their opportunities. With a strong footprint in Rwanda, Kenya, Uganda, Tanzania, DRC, and South Sudan, Equity Bank is now home to nearly 20 million customers - the largest customer base in Africa. Currently, the Bank is seeking additional talent to serve as SENIOR OFFICER, DATA SCIENCE AND ENGINEERING, whose holder reports to the Manager, Data Analytics

Job Purpose

The Data Science & Engineering role

is responsible for designing, developing, and maintaining scalable data pipelines, analytical solutions, and Artificial Intelligence (AI) and Machine Learning (ML) models that support data-driven decision-making across Equity Bank Rwanda. The role combines Data Engineering, Data Science, and Machine Learning capabilities to transform complex banking data into reliable datasets, predictive insights, and automated solutions that improve business performance, customer experience, risk management, and operational efficiency.

Key Responsibilities

Key Responsibilities

• Design, develop, and maintain reliable and scalable ETL/ELT data pipelines.
• Integrate data from core banking systems, digital channels, CRM, ERP, APIs, and other enterprise data sources.
• Build and maintain analytical datasets required for reporting, advanced analytics, and Machine Learning.
• Support the development and enhancement of the Bank's Enterprise Data Warehouse (EDWH) and analytical data platforms.
• Automate data ingestion, transformation, validation, and processing activities.
• Monitor and optimize data pipelines for performance, availability, and reliability.
• Data Science, AI & Machine Learning
• Analyze large and complex datasets to identify patterns, trends, anomalies, and business opportunities.
• Design, develop, test, and deploy predictive and Machine Learning models.
• Develop analytical solutions supporting key banking use cases including:
• o Credit risk and portfolio analytics
• o Customer segmentation and behaviour analysis
• o Customer churn and retention
• o Fraud and anomaly detection
• o Product recommendation and cross-selling
• o Revenue and profitability analysis
• o Business and operational forecasting
• Support implementation of Generative AI, Natural Language Processing (NLP), and intelligent automation use cases where applicable.
• Develop complex SQL queries, procedures, and scripts for data extraction, transformation, and analysis.
• Design data models and analytical structures supporting reporting, analytics and Machine Learning.
• Optimize queries and data-processing workloads for large-volume banking datasets.Implement automated data-quality controls to ensure accuracy, completeness, consistency and reliability of data.
• Identify data-quality issues and coordinate remediation with relevant data owners.
• Maintain data lineage, metadata and appropriate technical documentation.
• Ensure solutions comply with the Bank's Data Governance, Information Security and Data Privacy requirements.
• Support responsible and governed use of AI and Machine Learning within the Bank.

QUALIFICATIONS, SKILLS, EXPERIENCE AND ATTRIBUTES

QUALIFICATIONS, SKILLS, EXPERIENCE AND ATTRIBUTES

• Bachelor’s degree in data science, Computer Science, Software Engineering, Computer Engineering, Statistics, Mathematics, Information Technology or a related quantitative discipline.
• Master’s degree in data science, Artificial Intelligence, Machine Learning, or a related field is an added advantage.
• Relevant professional certifications in Data Science, Data Engineering, AI/ML or Cloud Technologies are an added advantage.
• Minimum 2–3 years of relevant experience in Data Science, Data Engineering, Machine Learning, Advanced Analytics, or a related field.
• Hands-on experience developing ETL/ELT pipelines, analytical datasets, and Machine Learning models.
• Strong experience working with large datasets, relational databases, SQL, and Python.
• Experience deploying analytical or Machine Learning solutions into production is an added advantage.
• Experience in banking, financial services, FinTech, or another regulated environment is an added advantage.