Data Scientist

26 Aug 2026 Data Scientist at Gulf African Bank Recruit candidates with Ease. 100% recruitment control with Employer Dashboard.We have the largest Job seeker visits by alexa rankings. Post a Job Company: Gulf African Bank Location: Kenya State: Nairobi Job type: Full-Time Job category: Administrative/Secretarial Jobs in Kenya Resubmit your Resume Today. Click Here to Start

• Design and develop predictive and prescriptive models for priority banking use cases, including 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 appropriate statistical, machine learning, forecasting, optimization and experimentation techniques to uncoverpatterns 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 to determine business value, feasibility, data availability, delivery effort, dependencies and compatibility with the Bank’s technology environment. • 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 appropriate to the Bank’s environment. • Monitor model accuracy, stability, drift, bias, fairness and operational performance, and implement remediation, retraining or retirement actions when required. • Maintain a complete inventory of analytical models, including ownership, purpose, status, dependencies, versions and review dates. • Establish and maintain fit-for-purpose 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 and 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.