Strathmore University is a Chartered University located in Nairobi, Kenya. It was the first multiracial and multi religious educational institution in English speaking Eastern Africa and more recently the first institute of higher learning to be ISO certified in East and Central Africa in 2004. Our mission is to provide all-round quality education in an atmo...

Responsibilities or duties

Data Pipelines and Reporting

• Contributes significantly to the creation of the data architecture in terms of projected and expected data needs, performance and efficiency KPIs.
• Scopes and stages work into well-defined milestones to avoid monolithic deliverable.
• Go-to expert in are or the codebase. Understands architecture of the entire systems and provides technical advice and weights on the technical decisions that impact whole project.
• Able to successfully design and build end-to-end solutions with guidance from experts in the fields.

Data Science Strategy and Planning

• Contribute to the development and implementation of data science strategies.
• Work with cross-functional teams to identify and prioritize data science requirements.
• Support in recommending and implementing new technologies to enhance data science capabilities.

Data Quality and Governance

• Ensure the accuracy and reliability of data through data profiling, cleansing, and validation.
• Collaborate with data governance teams to establish and maintain data quality standards.
• Acquire data from primary or secondary data sources, filter, and clean data, maintain databases/data systems, and ensure data quality.
• Research on governance trends, best practices and improves on existing implementations. Constantly looking for improvements on the previous iterations.

Advanced Analytics and Modeling

• Model, design, and implement AI algorithms using diverse sources of data.
• Design and implement rigorous evaluation pipelines for AI models including large language models (LLMs), retrieval-augmented systems, and task-specific models.
• Support in the development and maintenance of benchmarking datasets (e.g. agricultural Q&A, edge cases, contextual prompts) to support standardized model assessment.
• Lead technical safety testing of AI advisory systems, including hallucination detection, inappropriate content identification, and escalation logic.
• Support the development and testing of guardrails, disclaimers, and fallback mechanisms for farmer-facing advisory use cases.
• Design and analyse experiments (e.g. A/B testing, persona-based trials) to assess AI output quality, usability, and performance across different contexts.
• Work closely with Data Engineers and MLOps Engineers to ensure AI pipelines are reproducible, auditable, and well-documented.
• Explore, learn, and deliver more complex tasks, including robust scheduled code execution, building frameworks and Apis for the rest of the team and building event based data processing

Collaboration and Stakeholder Management

• Collaborate with internal and external stakeholders to gather business requirements and understand analytical needs.
• Provide support and training to end-users on utilizing data science tools and interpreting analytical outputs.
• Support in writing reports, documentations, and publications related to business intelligence.
• Liaise and work effectively with the software development team to ensure all data needs are well addressed in projects.
• Research new and emerging trends in data science to grow skills and facilitate client projects.

Project Management and Follow-up

• Achieve results through follow-up of projects through to completion.
• Monitor project progress, manage priorities, commit to achieving quality outcomes, adhere to documentation procedures, and seek feedback from stakeholders to gauge satisfaction.

Qualifications or requirements (e.g., education, skills)

Minimum Academic Qualifications:

• Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, Artificial Intelligence, or a closely related quantitative field.

Experience needed

Experience:

• Applicants should possess at least 5 years of progressive experience in data science, advanced analytics, or applied research roles, with demonstrated responsibility for complex analytical or modelling tasks.

• Contributes significantly to the creation of the data architecture in terms of projected and expected data needs, performance and efficiency KPIs.
• Scopes and stages work into well-defined milestones to avoid monolithic deliverable.
• Go-to expert in are or the codebase. Understands architecture of the entire systems and provides technical advice and weights on the technical decisions that impact whole project.
• Able to successfully design and build end-to-end solutions with guidance from experts in the fields.
• Contribute to the development and implementation of data science strategies.
• Work with cross-functional teams to identify and prioritize data science requirements.
• Support in recommending and implementing new technologies to enhance data science capabilities.
• Ensure the accuracy and reliability of data through data profiling, cleansing, and validation.
• Collaborate with data governance teams to establish and maintain data quality standards.
• Acquire data from primary or secondary data sources, filter, and clean data, maintain databases/data systems, and ensure data quality.
• Research on governance trends, best practices and improves on existing implementations. Constantly looking for improvements on the previous iterations.
• Model, design, and implement AI algorithms using diverse sources of data.
• Design and implement rigorous evaluation pipelines for AI models including large language models (LLMs), retrieval-augmented systems, and task-specific models.
• Support in the development and maintenance of benchmarking datasets (e.g. agricultural Q&A, edge cases, contextual prompts) to support standardized model assessment.
• Lead technical safety testing of AI advisory systems, including hallucination detection, inappropriate content identification, and escalation logic.
• Support the development and testing of guardrails, disclaimers, and fallback mechanisms for farmer-facing advisory use cases.
• Design and analyse experiments (e.g. A/B testing, persona-based trials) to assess AI output quality, usability, and performance across different contexts.
• Work closely with Data Engineers and MLOps Engineers to ensure AI pipelines are reproducible, auditable, and well-documented.
• Explore, learn, and deliver more complex tasks, including robust scheduled code execution, building frameworks and Apis for the rest of the team and building eventbased data processing
• Collaborate with internal and external stakeholders to gather business requirements and understand analytical needs.
• Provide support and training to end-users on utilizing data science tools and interpreting analytical outputs.
• Support in writing reports, documentations, and publications related to business intelligence.
• Liaise and work effectively with the software development team to ensure all data needs are well addressed in projects.
• Research new and emerging trends in data science to grow skills and facilitate client projects.
• Achieve results through follow-up of projects through to completion.
• Monitor project progress, manage priorities, commit to achieving quality outcomes, adhere to documentation procedures, and seek feedback from stakeholders to gauge satisfaction.

• AI model development
• Benchmarking
• Safety testing
• Applied analytics
• Data ingestion frameworks
• Data architecture
• Data pipelines
• Reporting
• AI algorithms
• Large language models (LLMs)
• Retrieval-augmented systems
• Task-specific models
• Benchmarking datasets
• Technical safety testing
• Hallucination detection
• Inappropriate content identification
• Escalation logic
• Guardrails
• Disclaimers
• Fallback mechanisms
• Experiment design and analysis (A/B testing, persona-based trials)
• Reproducible AI pipelines
• Auditable AI pipelines
• Well-documented AI pipelines
• Scheduled code execution
• Framework building
• API building
• Event-based data processing
• Business requirement gathering
• Analytical needs understanding
• Data science tools utilization
• Analytical output interpretation
• Report writing
• Documentation
• Publications
• Business intelligence
• Software development liaison
• Emerging trend research
• Project management
• Stakeholder management

• Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, Artificial Intelligence, or a closely related quantitative field.