Posted: 2026-08-26 20:54:14  Deadline: September 01, 2026   Full-time   Salary: Confidential

Gulf African Bank is inviting applications from qualified candidates for the position of Data Scientist within the Data and Analytics Department. The successful candidate will design, develop, deploy and govern advanced analytics, machine learning and artificial intelligence solutions that support strategic decision-making, strengthen risk management, improve customer outcomes, optimize financial performance and enhance operational efficiency across the Bank.

Position Details

Position: Data Scientist.
Department: Data and Analytics Department.
Reports to: Head of Data and Analytics.
Location: Head Office - Nairobi, Kenya.
Direct Reports: None.
Customers of the Position: All Departments in the Bank.

Key Responsibilities

Design and develop predictive and prescriptive models for customer behaviour, credit and portfolio risk, fraud and anomaly detection, customer churn, collections, deposit attrition, product propensity, profitability and branch or channel optimization.
Perform data discovery, preparation, feature engineering, model training, validation, tuning and testing using structured and unstructured banking data.
Apply statistical, machine learning, forecasting, optimization and experimentation techniques to uncover patterns and support evidence-based decisions.
Translate complex analytical outputs into clear recommendations, decision rules, dashboards and interventions for business and control functions.
Evaluate requests for new or modified analytical solutions based on business value, feasibility, data availability, delivery effort, dependencies and technology compatibility.
Develop proof-of-concepts and pilots to assess emerging analytics and AI approaches before scaled implementation.
Collaborate with Data Engineering and Technology teams to productionize models and analytical solutions using scalable and controlled deployment practices.
Apply version control, reproducible development, testing, release management and model lifecycle practices.
Monitor model accuracy, stability, drift, bias, fairness and operational performance, implementing remediation, retraining or retirement when required.
Maintain an inventory of analytical models, including ownership, purpose, status, dependencies, versions and review dates.
Establish and maintain model documentation covering methodology, assumptions, limitations, data sources, validation results, performance thresholds and intended use.
Support independent model review and validation by providing evidence, test results and traceable documentation.
Ensure analytics and AI solutions comply with applicable laws, regulations, internal policies, information security requirements, data protection obligations, model risk controls and responsible AI principles.
Assess and communicate model limitations, explainability, potential bias and risks to relevant stakeholders before implementation.
Maintain audit-ready records and ensure material model changes are appropriately reviewed and approved.
Partner with Business, Finance, Risk, Compliance, Operations and Technology stakeholders to define business problems, analytical hypotheses, success measures and implementation requirements.
Quantify and track the benefits of analytics initiatives, including revenue uplift, cost reduction, risk mitigation, customer retention, productivity improvement and turnaround-time reduction.
Prioritize analytical use cases based on strategic alignment, expected value, feasibility, data readiness, risk and implementation effort.
Act as a trusted analytical advisor by challenging assumptions and converting findings into practical actions and decisions.
Promote adoption of Business Intelligence tools, analytical frameworks and self-service analytics across the Bank.
Deliver performance dashboards and analytical insights covering deposits, financing growth, revenue streams, profitability, portfolio quality, customer performance, products, branches and channels.
Integrate predictive outputs into management reporting and performance monitoring where they improve forward-looking decision-making.
Work with Data Governance, Data Engineering and business data owners to improve data quality, lineage, accessibility, security and usability.
Define analytical data requirements, critical data elements, quality rules and remediation priorities for key data science use cases.
Contribute to the enhancement of the Bank’s analytics architecture, tools, development environments and scalable data science platforms.
Contribute to the Bank’s advanced analytics and AI roadmap, including opportunities for machine learning, natural language processing, generative AI and intelligent automation.
Develop and maintain user guides, technical documentation, model cards, operating procedures and knowledge repositories.
Deliver user training and knowledge-sharing sessions to help stakeholders understand, interpret and responsibly use analytical outputs.
Mentor junior analysts and contribute to professional standards, peer review and continuous capability development.
Represent Data and Analytics in relevant project teams, governance forums and cross-functional working groups.
Ensure compliance with all applicable laws, regulations, Bank policies and procedures.

Required Skillset

Banking and financial services knowledge: 20%.
Statistics, mathematics and experimental design: 15%.
Programming, SQL and machine learning: 25%.
Data engineering fundamentals: 10%.
MLOps, deployment and model monitoring: 5%.
Business Intelligence, visualization and data storytelling: 10%.
Data governance, model risk, privacy and responsible AI: 5%.
Business analysis, communication and stakeholder management: 10%.

Core Tools and Methods

Python or R.
SQL.
Power BI.
Microsoft Excel or equivalent visualization tools.
Statistical modelling.
Supervised and unsupervised learning.
Forecasting and anomaly detection.
Version control and cloud-based analytics platforms, subject to the Bank’s approved technology environment.

Education and Experience

Bachelor’s degree in Statistics, Applied Mathematics, Data Science, Computer Science, Actuarial Science, Economics, Financial Engineering, Operations Research or another relevant quantitative discipline.
A Master’s degree in Data Science, Statistics, Artificial Intelligence, Business Analytics, Financial Engineering or a related field is an added advantage.
Relevant professional certifications in data science, cloud analytics, machine learning, business intelligence, data management, risk or banking are an added advantage.
Minimum of 3 to 5 years’ relevant experience in banking analytics, data science, risk analytics, customer analytics or a closely related quantitative role.
Demonstrable experience delivering analytical solutions from business problem definition through model development, deployment, adoption and benefits tracking.

Key Behavioral Competencies

Analytical thinking, intellectual curiosity and sound professional judgement.
Ability to communicate complex analysis clearly to technical, business and executive audiences.
Collaboration, stakeholder influence and constructive challenge.
Attention to detail, documentation discipline and a strong control mindset.
Commercial awareness, delivery focus and accountability for measurable outcomes.
Commitment to ethical, fair and responsible use of data and AI.

Application Process

Interested and qualified candidates should submit their applications by September 01, 2026 through the application link below.

Application Link: Data Scientist at Gulf African Bank (Instructions and Application)