SUMMARY:
Gijima is looking for a senior Data Strategist to lead the design and execution of our enterprise data and AI strategy. This role will shape how data is governed, architected, and used across the Group to drive smarter decisions, operational excellence, and digital transformation at scale. You’ll work closely with executive leadership and technology teams to turn business priorities into high-impact data, analytics, and AI solutions.

POSITION INFO:

Job Purpose

Formal Education

1. Grade 12
2. A degree in Data Science, Computer Science, or related field.

Work Experience

1. Minimum 10 years of experience in the ICT industry.
2. Minimum 5 years of experience in data analysis, science or strategy.
3. Strong understanding of data modeling, machine learning, and statistics.
4. Proficiency in data visualization tools and programming languages.
5. Excellent communication and project management skills.
6. Business acumen and understanding of industry trends.
7. Experience with data governance and quality assurance.
8. Strong problem-solving and analytical skills.
9. Experience with cloud based data platforms.

Key Skills and Capabilities

1. Strategic thinking and planning.
2. Technical Skills in terms of Data Extraction, Data Preparation, Data Visualisation, Data Warehousing and Data Modelling based on complex data sets.
3. Mathematical and statistical skills and experience.
4. Management Consulting Skills.
5. Analytical Skills inclusive of Critical Thinking and Problem Solving.
6. High attention to detail.
7. Verbal and written Communication Skills.
8. Collaboration at all levels in the organisation.
9. Project Management Skills.
10. Research capability and experience.

Key Responsibilities

1. Data Strategy: Develop and implement comprehensive data strategies aligned with business objectives.
2. Projects: Lead data-related projects, ensuring effective data management, analysis, and visualization.
3. Collaboration: Collaborate with cross-functional teams to integrate data insights into business decision-making and strategic initiatives.
4. Data Management: Develop and manage data management plans, ensuring data quality and integrity.
5. Data Architecture: Collaborate with IT teams to design and implement data infrastructure and architecture.
6. Data Identification: Identify reliable data sources for both structured and unstructured data, including API’s, Databases, Sensors, etc…
7. Data Collection: Collect Data from identified sources.
8. Data Preparation: Normalise and standardise data formats, schemas, and naming conventions to facilitate data integration. Clean, Transform and Enrich data for including or use by Algorithms.
9. Data Ingestion / Integration: Integration of data from different sources into a data lake / data repository.
10. Data Analysis and Exploration: Analyse data to identify patterns, gain insights and discover potential problems and/ or opportunities.
11. Data Modelling and Evaluation: Use high quality data to train, test and evaluate the AI Models that will solve the identified problems or achieve the required objectives.
12. Model Deployment and Monitoring: Deploy the model and implement monitoring to track performance, detect anomalies and mitigate risk in real time.
13. Documentation and Knowledge Sharing: Document the Data Architecture and share knowledge with key stakeholders.
14. Continuous improvement: Obtain feedback on model performance and outcomes and use information to improve the Data Model.
15. Data Quality and Governance: Establish Standards and Policies to ensure data accuracy, completeness, and consistency.
16. Trends: Stay up-to-date with industry trends, tools, and methodologies, applying best practices.

Personal Attributes

1. Problem-solving and analytical skills
2. Creativity and innovation
3. Effective communication
4. Collaboration and teamwork
5. Strategic thinking and vision
6. Integrity
7. Dependable