Data Scientist
AI summary
Gulf African Bank is hiring a Data Scientist in Nairobi to design, deploy and govern advanced analytics, machine learning and AI solutions across banking functions. The role focuses on predictive and prescriptive modelling, model deployment and monitoring, responsible AI practices, and cross-functional collaboration to support data-driven decision-making.
- Build predictive and prescriptive models for credit risk, fraud detection, churn and customer analytics
- Productionize models with Data Engineering and Technology teams using MLOps practices
- Ensure responsible AI, model governance, compliance and audit-ready documentation
- Partner with Business, Finance, Risk, Compliance and Operations stakeholders
- Translate complex analytics into clear business recommendations and dashboards
AI job guide
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AI salary guide
Not enough public dataNot enough public salary data is available for this exact role. Before applying, prepare to ask about gross pay, benefits, contract length, probation period, transport and any allowances.
Can you qualify for this role?
- UnclearRelated work experienceThe text mentions experience, but the exact level should be confirmed at source.
- RequiredEducation or certification mentioned in the postThe captured text mentions education, a diploma, certificate, or licence.
- PreferredPractical evidence in security, internship, entregadorThe tags and summary point to skills connected with this role.
- RequiredAvailability to work in NairobiThe vacancy is associated with this location.
- UnclearComfort with the Full Time contract termsConfirm hours, duration, probation and benefits at the original source.
Documents to prepare
- Likely requiredUpdated CV
- Role specificCover letter or short employer message
- OptionalProfessional references
- Role specificAcademic or professional certificates
- VerifyID or passport only after verifying the employer
Application tips for this job
- Place your strongest Data Scientist evidence in the first half of your CV.
- In your cover letter or employer message, connect your experience to Gulf African Bank and the role in Nairobi.
- Add concrete examples related to security, internship, entregador, ideally with measurable outcomes or clear responsibilities.
- Follow the instructions from JobWeb Kenya; avoid sending documents to unofficial contacts or copied links.
- Confirm the deadline, interview location and employer contact before sharing personal documents.
- Prepare a polite question about pay, benefits and contract terms for later interview stages.
Source and safety check
- JobWeb Kenya
- Original source link available
- Application method is clear
- Deadline not specified
- No major risk signal was detected in the captured text.
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Interview preparation
- What experience makes you a strong fit for this Data Scientist role in security, internship?
- How have you handled responsibilities similar to those in this job post?
- Are you available to work in Nairobi under the listed contract or schedule?
- Prepare examples with clear responsibilities, tools used and measurable outcomes.
- Review the source and research Gulf African Bank before the interview.
Ask what the first priorities will be in the role and how success will be measured.
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Original source description
Job Purpose
To 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.
The role will leverage structured and unstructured data to build predictive and prescriptive models, translate complex analysis into actionable business recommendations, and embed scalable, responsible and measurable analytics solutions into business processes. The job holder will partner with Business, Finance, Risk, Compliance, Operations and Technology teams to strengthen the Bank’s advanced analytics capability and promote data-driven decision-making.
Key
Responsibilities
Advanced Analytics & Data Science
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.
Model Deployment, Monitoring & MLOps
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.
Model Governance, Responsible AI & Compliance
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.
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 using 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.
Work with business owners to formulate key business questions, define consistent metrics and build trusted datasetsthat answer those questions.
Integrate predictive outputs into management reporting and performance monitoring where they improve forwardlooking decision-making.
Business Performance Analytics Support
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.
Work with business owners to formulate key business questions, define consistent metrics and build trusted datasets that answer those questions.
Integrate predictive outputs into management reporting and performance monitoring where they improve forwardlooking decision-making.
Data Quality, Management & Platform Enablement
Work with Data Governance, Data Engineering and business data owners to improve the quality, lineage, accessibility, security and usability of data used for analytics.
Define analytical data requirements, critical data elements, quality rules and remediation priorities for key data science use cases.
Improve procedures, guidelines, standards and reusable practices for analytical data management, maintenance, reporting and security.
Contribute to the enhancement of the Bank’s analytics architecture, tools, development environments and scalabledata science platforms.
Capability Development, Innovation & Stakeholder Engagement
Contribute to the Bank’s advanced analytics and AI roadmap and identify high-value 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 for analytical solutions.
Deliver user training and knowledge-sharing sessions to enable stakeholders to understand, interpret and responsibly use analytical outputs.
Mentor junior analysts and contribute to professional standards, peer review and continuous capability development within the Data & Analytics function.
Represent Data & Analytics in relevant project teams, governance forums and cross-functional working groups.
Governance & Compliance
Ensure compliance with all applicable laws and regulations, as well as with the Bank’s internal policies and procedures while executing assigned duties and responsibilities.
Perform any other duties that may be assigned by management, consistent with the purpose and level of the role.
Key Relationships
Direct Reports to this Position
None
Customers of this Position
All Departments in the Bank
Required Skillset mix
The job holder should demonstrate a balanced combination of banking, quantitative, technical and stakeholdermanagement capability:
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 may include Python or R, SQL, Power BI, Microsoft Excel or equivalent visualization tools, statistical modelling, supervised and unsupervised learning, forecasting, anomaly detection, version control and cloudbased analytics platforms, subject to the Bank’s approved technology environment.
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.
Education
Qualifications 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.
- Master’s degree in Data Science, Statistics, Artificial Intelligence, Business Analytics, Financial Engineering or a related field will be an added advantage.
- Relevant professional certifications in data science, cloud analytics, machine learning, business intelligence, data
- management, risk or banking will be an added advantage.
- Minimum of 3 to 5 years’ relevant
- in banking analytics, data science, risk analytics, customer analytics or a closely related quantitative role.
- Demonstrable
- delivering analytical solutions from business problem definition through model development, deployment, adoption and