CRDB Bank PLC is recruiting 2 Data Scientists to join its Data Management Office at the Tanzania Head Office.

Reporting to the Senior Manager – Advanced Analytics and Machine Learning, the Data Scientist will design, develop, validate, deploy and continuously improve data science and machine learning solutions that deliver measurable business value across banking operations.

The role combines advanced analytics with strong governance responsibilities. Successful candidates will develop solutions for areas including fraud detection, credit risk, collections, customer analytics and operational efficiency, while ensuring models remain accurate, explainable, fair, secure and compliant throughout their lifecycle.

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AVAILABLE VACANCIES

1–2. Data Scientist

Employer: CRDB Bank PLC
Department: Data Management Office
Location: Tanzania Head Office
Reporting To: Senior Manager – Advanced Analytics and Machine Learning
Employment: Permanent
Openings: 2

KEY RESPONSIBILITIES

The successful candidates will:

• Design, develop and implement predictive, prescriptive and optimization models.
• Develop analytical solutions for priority banking use cases.
• Support fraud-detection modelling.
• Support credit-risk assessment.
• Develop solutions for collections.
• Support customer analytics.
• Develop models and analytics supporting operational efficiency.
• Translate business problems into practical data-science use cases.
• Define success criteria with relevant stakeholders.
• Ensure solutions align with approved business objectives and governance requirements.
• Perform data exploration.
• Conduct feature engineering.
• Train and test machine-learning models.
• Evaluate model performance using appropriate statistical and machine-learning techniques.

MODEL DOCUMENTATION & GOVERNANCE

The Data Scientist will prepare comprehensive model documentation covering:

• Business rationale.
• Methodology.
• Assumptions.
• Data sources.
• Feature definitions.
• Model limitations.
• Performance metrics.
• Implementation considerations.

Documentation must support model review, approval, audit and regulatory scrutiny.

Candidates will also ensure models comply with the Bank's:

• AI governance framework.
• Model risk-management standards.
• Data-governance requirements.
• Responsible AI principles.
• Regulatory obligations.

RESPONSIBLE AI & MODEL RISK

The role requires candidates to identify, assess and mitigate risks relating to:

• Model bias.
• Unfair outcomes.
• Data quality.
• Customer privacy.
• Model explainability.
• Model robustness.
• Misuse of analytical solutions.

Material issues must be escalated through the appropriate governance channels.

MACHINE LEARNING OPERATIONS

The Data Scientist will collaborate with:

• Data Engineering.
• MLOps.
• IT.
• Risk.
• Compliance.
• Information Security.
• Internal Audit.
• Business teams.

This collaboration will support controlled model deployment, integration, monitoring and change management.

PRODUCTION MODEL MONITORING

Candidates will monitor models and analytical solutions in production for:

• Performance.
• Stability.
• Model drift.
• Fairness.
• Operational effectiveness.

Where necessary, the Data Scientist will recommend:

• Recalibration.
• Retraining.
• Rollback.
• Model retirement.

VERSION CONTROL & AUDITABILITY

The successful candidates will maintain appropriate:

• Version control.
• Data traceability.
• Audit trails.
• Dataset records.
• Code records.
• Experiment records.
• Model versions.
• Approval records.
• Production-change records.

The role also requires appropriate controls around data confidentiality, customer privacy, access management and secure handling of sensitive information.

ADVANCED DATA SCIENCE

Where justified by business requirements, data readiness and governance approval, candidates may support experimentation involving:

• Time-series forecasting.
• Natural Language Processing – NLP.
• Deep learning.
• Advanced machine-learning techniques.

The role will also contribute to model inventories, periodic reviews, performance reporting and governance forums.

QUALIFICATIONS & EXPERIENCE

Candidates should have:

• Bachelor's Degree in Computer Science, Statistics, Mathematics, Data Science or a related field.
• Minimum 3 years of experience in machine learning, statistical modelling or data analysis.
• Demonstrated experience in machine learning.
• Data-engineering experience.
• AI-governance experience.
• Experience demonstrating business-value realization from data or AI solutions.

Added Advantage

A Master's Degree in any of the following will be an added advantage:

• Data Science.
• Artificial Intelligence.
• Machine Learning.
• Statistics.
• Business Analytics.
• MBA with specialization in Analytics.

PROFESSIONAL CERTIFICATIONS – MANDATORY

The vacancy states that professional certifications in the following areas are mandatory:

• Azure AI
• Data Science
• MLOps
• Responsible AI

Candidates should ensure applicable professional certifications are clearly presented in their application.

REQUIRED TECHNICAL SKILLS

Programming & Data

• Python
• SQL

Machine Learning Frameworks

Candidates should have proficiency with frameworks such as:

• Scikit-learn
• XGBoost
• TensorFlow
• PyTorch

Statistics

Strong understanding of:

• Statistics.
• Probability.
• Data-science principles.

Data Visualization

Experience with tools such as:

• Tableau
• Power BI
• Matplotlib
• Seaborn

Cloud Machine Learning

Familiarity with cloud-based machine-learning solutions on:

• Microsoft Azure
• AWS
• Google Cloud Platform – GCP

APPLICATION DETAILS

Company: CRDB Bank PLC
Position: Data Scientist
Department: Data Management Office
Reporting Line: Senior Manager – Advanced Analytics and Machine Learning
Location: Tanzania Head Office
Number of Openings: 2
Minimum Experience: 3 years
Employment Terms: Permanent
Salary: Not specified
Deadline: 5 October 2026

HOW TO APPLY

Qualified candidates should submit their application through the official CRDB Bank recruitment portal using the APPLY option associated with this vacancy.

Your application should clearly demonstrate relevant experience in:

• Python and SQL.
• Machine learning.
• Statistical modelling.
• Data analysis.
• AI governance.
• Data engineering.
• MLOps.
• Cloud machine learning.
• Model validation and monitoring.
• Data visualization.
• Responsible AI.

📅 Application Deadline: 5 October 2026