Manager Artificial Intelligence Portfolio Delivery

• Lead selection and review of statistical and machine learning methods, testing assumptions, suitability, complexity and alternatives before development proceeds.
• Direct feature design, transformation and reusable pipelines; review data quality, lineage, leakage risks and consistency between training and production inputs.
• Review Python and SQL code, experiments and pipeline designs; require reproducibility, version control, automated tests and peer review before handover.
• Challenge model results using appropriate test splits, benchmarks and error analysis; assess calibration, explainability, fairness and robustness where relevant.
• Guide retrieval, prompting and evaluation for generative AI and bounded agent workflows; test grounding, source permissions, tool limits and human oversight.
• Resolve complex modelling and feature-pipeline problems through hands-on analysis, code review and targeted prototyping with the team.
• Define technical handover requirements with ML Engineering and ML Ops, covering scoring consistency, interfaces, performance, monitoring and rollback readiness.
• Review drift, model-quality and feature-quality evidence with Model Owners; recommend remediation or retraining and route material changes through required gates.
• Ensure complete development evidence, limitations and control documentation; address validation findings without substituting team review for independent validation.
• Manage portfolio scope, resources, budgets, dependencies and supplier technical deliverables; report quality, delivery risks and benefits against agreed baselines.
• Coordinate business acceptance and adoption with business owners and the AI Business Analyst & Adoption role; distinguish measured value from forecasts.
• Set staff objectives, review technical performance and coach Data Scientists and Feature Engineers; strengthen specialist capability, shared practices and succession.
• Establish and continuously improve standards for model development, feature engineering, experimentation, coding practices, documentation, and quality assurance to promote consistency and engineering excellence across the AI portfolio.
• Collaborate with Data Engineering, ML Engineering, MLOps, ICT, Architecture, Cybersecurity, and business stakeholders to ensure successful delivery, operational readiness, and long-term sustainability of AI solutions.
• Monitor portfolio performance, delivery milestones, technical risks, resource utilization, and dependencies, implementing corrective actions to maintain delivery commitments and quality standards.
• Support compliance with the Bank’s AI Governance Framework, Model Risk Management Standards, Data Governance Requirements, Responsible AI Principles, and applicable regulatory obligations.
• Evaluate emerging artificial intelligence, machine learning, feature engineering, analytics, and generative AI techniques and recommend opportunities to enhance business value, model performance, and delivery effectiveness.
• Promote innovation, knowledge sharing, technical mentoring, and continuous learning across Data Scientists and Feature Engineers to strengthen organizational AI capabilities
• Bachelors degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, Data Engineering, Engineering, Analytics, Information Systems, or a related quantitative discipline.
• A Master's degree in Data Science, Artificial Intelligence, Machine Learning, Statistics, Computer Science, Engineering, Analytics, Business Analytics, Technology Management, MBA, or a related field will be an added advantage.
• Professional certifications in Artificial Intelligence, Machine Learning, Data Science, Cloud Computing, Data Engineering, Agile Delivery, Project Management, Product Management, or related disciplines will be an added advantage.
• Relevant advanced AI, data engineering or cloud certification is an advantage; delivery certification is supplementary.
• Minimum of 5 years of hands-on data science, machine learning, artificial intelligence, advanced analytics, feature engineering, or data engineering experience.
• Minimum of 3 years of experience leading Data Scientists, Feature Engineers, analytics teams, or complex AI and data delivery initiatives.
• Strong Python, SQL, statistics, model evaluation and feature-pipeline skills; proven code-review and troubleshooting ability.
• Knowledge of banking operations, digital banking, customer analytics, fraud detection, credit risk, collections, financial crime, or related financial services use cases will be an added advantage.
• Experience delivering predictive and generative AI, reproducible experiments, reliable pipelines and monitored production solutions.
• Ability to challenge designs, mentor specialists and balance technical quality, delivery priorities, costs and business outcomes.
• Strong knowledge of statistical modelling, machine learning algorithms, feature engineering, model monitoring, explainability techniques, bias assessment, and model lifecycle management.
• Experience working with cloud platforms, modern data platforms, MLOps practices, version control systems, experiment tracking tools, and collaborative software development environments.

CRDB Bank is dedicated to upholding Sustainability and ESG practices and encourage applicants who share this commitment. The Bank also promotes an inclusive workplace, hence applications from women and individual with disabilities are encouraged. It is important to note that CRDB Bank does not charge any fees for the application or recruitment process, and any requests for payment should be disregarded as they do not represent the bank’s practices. Only Shortlisted Candidates will be Contacted.