New Vacancies at CRDB Bank October 2026

The Manager, AI Portfolio Delivery leads Data Scientists and Feature Engineers, owning technical quality and delivery. The role directs modelling and feature engineering, reviews code and evidence, resolves complex problems and coaches specialists. It coordinates portfolio delivery and engineering handover while preserving independent validation and formal approval.

• Set the technical direction and delivery plan for Data Scientists and Feature Engineers, aligning work with business priorities, data readiness and available capacity.
• Translate business problems into modelling objectives, baselines, measurable acceptance criteria and feasible technical solution designs.
• 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.

The Specialist, Data Integration is responsible for designing, developing and continuously improving secure, reliable and scalable data integration solutions that enable timely, accurate and controlled movement of data across CRDB’s source systems, governed data platforms and authorized consumers.

The role delivers batch, ETL/ELT, change data capture, streaming, manage file exchange and approved data API integrations in line with the Bank’s architecture, security, data governance and service standards.

The role, it provides Level 2 and Level 3 support for complex or recurring integration failures, leads root-cause analysis and implements permanent corrective solutions. Routine production monitoring, scheduled reruns and first-line recovery remain with Data Operations unless otherwise assigned through the approved service model.

• Design, develop and unit-test batch ETL/ELT, CDC and streaming pipelines (e.g., ODI, Oracle GoldenGate, Kafka, NiFi, Informatica or lakehouse-native tools) from source to staging, warehouse, data marts and lakehouse, following approved architecture, naming conventions and idempotent-load standards.
• Onboard new data sources through the data source registration process: analyse source structures, agree extraction method and schedule with System Custodians, and produce source-to-target mappings and interface specifications.

• Monitor daily loads, replication and streaming lag; resolve failed jobs within agreed SLAs; manage reruns and backfills with full audit trail; and drive permanent fixes so the Bank depends less on manual intervention.

• Lead root-cause analysis for recurring integration incidents (e.g., broken connections, schema drift, constraint violations, Kafka/OGG lag, stuck extractions) and track corrective actions to closure with DICT and vendors.
• Build reconciliation and validation controls into every pipeline (row counts, hash/control totals, null, uniqueness and referential checks, schema-drift alerts), and quarantine and escalate failed data per the data quality SOP.
• Tune extraction and load performance (incremental loads, partitioning, parallelism, query optimisation) with DBAs to meet batch windows and real-time latency targets as volumes grow.
• Build and maintain governed APIs, file and message interfaces for internal consumers, regulators (e.g., RTSIS) and approved third parties, with security, versioning and service levels defined.
• Apply least-privilege service accounts, encryption in transit, masking of personal data in non-production, and segregation between development, UAT and production, in line with Information Security policy and the Personal Data Protection Act 2022.
• Deliver all changes through version control, UAT and change management; maintain run-books, interface catalogues and technical documentation; and supply integration metadata and lineage to the Master Data & Metadata Unit.
• Contribute to DMP/lakehouse migration, ODI and legacy interface rationalization and MDM synchronization (inbound CDC, outbound golden-record publication), and take over knowledge from vendors and implementation partners.

• Bachelors degree in Computer Science, Information Technology, Software Engineering, Information Systems, or a related field.
• Professional certifications such as Oracle (ODI/GoldenGate/Database), Confluent Kafka, Informatica, Databricks/Cloudera or cloud data engineering certifications, Certified Data Management Professional (CDMP), or DCAM certification are an added advantage.
• Minimum of 3 years of experience in data integration, data engineering, ETL/ELT development, API integration, or enterprise data management within a complex technology environment.
• Knowledge of T24 core banking data structures, Bank of Tanzania regulatory reporting requirements (including RTSIS), Personal Data Protection Act 2022, and ITIL incident, problem, and change management practices is an added advantage.
• Strong knowledge of DAMA-DMBOK2 Data Integration & Interoperability, Data Architecture, Data Modelling, Data Quality, Data Security, Metadata Management, and EDM Council DCAM data and technology architecture capabilities.
• Knowledge of integration architectures and patterns, including batch ETL/ELT, CDC/replication, event streaming, publish-subscribe, APIs/REST, file transfer, hub-and-spoke and point-to-point designs, as well as data warehouse, lakehouse, and dimensional modelling concepts.
• Advanced SQL and PL/SQL development and performance tuning on Oracle (Exadata) and SQL Server.
• Hands-on experience with ETL/ELT, replication and streaming technologies such as Oracle Data Integrator, Oracle GoldenGate, Informatica, SSIS, Apache Kafka and NiFi.
• Proficiency in Python, Shell scripting, and data processing automation tools; experience with Spark/PySpark is an added advantage.

• Experience with API design and integration (REST, JSON/XML), job scheduling/orchestration, version control (Git), and CI/CD practices for data pipelines.
• Strong analytical, troubleshooting, documentation, and communication skills, with the ability to explain technical concepts and issues to business and management stakeholders..

Only Shortlisted Candidates will be Contacted.