Senior Officer, Data Science And Engineering

SENIOR OFFICER, DATA SCIENCE AND ENGINEERING

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 and Accountability

Data Engineering & Integration

• 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.

Model Deployment & MLOps

• Support deployment and integration of Machine Learning models into production systems and business processes.
• Implement model versioning, monitoring, and performance tracking.
• Monitor production models for performance degradation and data drift.
• Support automated model training, testing, deployment, and retraining processes.

Data Modelling & Warehousing

• 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.

Data Quality, Governance & Security

• 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.

Business Engagement & Continuous Improvement

• Work with business units to identify opportunities where data, analytics and AI can solve business problems and improve decision-making.
• Translate business requirements into scalable data and analytical solutions.
• Present analytical findings and recommendations clearly to technical and non-technical stakeholders.
• Monitor production data and analytical solutions and resolve related incidents.
• Continuously identify opportunities for automation, optimization and innovation using emerging data and AI technologies.

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.

Technical Skills

• Python and SQL for data analysis, processing and automation.
• Machine Learning, predictive modelling and statistical analysis.
• ETL/ELT, data pipelines, data warehousing and data modelling.
• Strong knowledge of Oracle, SQL Server and/or MySQL databases.
• Data visualization and BI tools, preferably Power BI.
• APIs and system integration.
• Data quality, governance and security principles.
• Knowledge of model deployment and Cloud/AI technologies.

Core Competencies

• Strong analytical, problem-solving and quantitative skills.
• Ability to translate business needs into data and AI solutions.
• Strong focus on data quality, accuracy, security and confidentiality.
• Effective communication and stakeholder management skills.
• Strong ownership, accountability and ability to work independently.
• Innovative and continuous-learning mindset.

If you meet the above requirements, submit your application by 17th September 2026. Please include an updated Curriculum Vitae, copies of the relevant certificates and testimonials.

All applications should be in soft copy and through the Link Indicated below, and only shortlisted candidates will be contacted:

Link: https://equitybank.taleo.net/careersection/ext_new/jobsearch.ftl

Equity Bank is an equal opportunity employer. We value the diversity of individuals, ideas, perspectives, insights, values, and what they bring to the workplace.