Head, Data, Analytics and AI

About the role

Kenya Airways is looking for a Head, Data, Analytics and AI (Head-DNA) to lead its transformative initiatives in data, analytics and artificial intelligence. Reporting to the Chief Information and Data Officer (CIDO), the role manages the strategic, technical and operational aspects of the DNA department, recruits top talent and contributes to the airline's growth. The position is based in Nairobi.

Key responsibilities

• Work with the Data Privacy Officer so that PII data is classified and managed in line with legal compliance.
• Assess the current state, future state and gap analysis of the enterprise's data maturity compared with peers.
• Set and enforce data governance, compliance, quality and security best practices.
• Oversee the design, development and maintenance of scalable data pipelines and infrastructure, and ensure data quality, integrity and security.
• Drive the research, development and deployment of AI and machine learning models, and their integration into business processes.
• Develop the strategic vision for data analytics and act as subject matter expert on statistical analysis, experimental design and financial impact analysis.
• Plan and manage a USD 2.5 million budget over a 3- to 5-year period aligned with the data strategy.
• Ensure data needs are met across the business and manage SLAs and support contracts with data science and analytics partners.
• Foster a data-driven culture and provide training on data governance and data stewardship.

Required profile

• Bachelor's degree in Statistics, Computer Science, Information Technology, Data Science, Engineering or another quantitative field; a Master's is an advantage.
• 7+ years of experience in data engineering, data science and/or AI, including at least 5 years in a leadership role.
• Proven track record of leading data science, data engineering and/or AI teams, with strong leadership, communication and interpersonal skills.

Required skills

• Machine learning frameworks, generative AI and big data platforms.
• Data architecture, governance and security best practices.
• Cloud platforms such as AWS, Azure or Google Cloud.
• Programming languages such as Python, R, Java and SQL.
• Big data technologies (Redshift, S3, Databricks, data lakes, Spark).
• Experience with Looker, Tableau or Power BI.