Description
Artificial Intelligence

• Collaborate with stakeholders to identify opportunities for data and analytics driven solutions.
• Lead the design, implementation, and use of artificial intelligence / machine learning and analytics.
• Design and incorporate best practices of “MLOps” for managing the lifecycle of models / solutions. E.g. data preparation, model training, feature selection, evaluation, model selection, deployment, monitoring, maintaining, code version control, etc.

Data Engineering (incl. Data Management)

• Design and help build a modern data platform with a robust data governance framework.
• Incorporate best practices of managing “big data” (i.e. high volume, high velocity, high variety).
• Establish and execute processes for data quality, metadata management, and data lifecycle management.
• Ensure adherence to relevant data privacy and regulatory requirements.

Leadership

• Serve as a trusted advisor to the business, providing insights and recommendations based on data.
• Advocate for data-driven decision-making across Teraco.
• Drive a culture of innovation and continuous learning and teaching – both within the Data & Analytics team and within the broader Teraco business.
• Stay on top of emerging trends in data science, AI, and analytics.
• Mentor and develop team members and stakeholders, promoting professional growth and expertise.

Value

• Measure and communicate the business impact of data and analytics initiatives to stakeholders.
• Continuously optimise data and analytics processes for efficiency and effectiveness.

Requirements
Skill Requirements

• Excellent written and verbal communication skills.
• Clearly articulates ideas and thoughts.
• Active listening and the ability to understand complex requirements.
• Problem-solving mindset with a proactive approach.
• Capability to work independently and as part of a team.
• Self-motivated and professional demeanour.
• Leadership qualities with high tolerance and patience.
• Commitment to self-development and continuous learning.
• Attention to detail and accuracy.
• Good prioritisation and organisation skills.
• Complies with policies and procedures.

Qualifications and Experience

• Degree in Mathematics, Data Science, Statistics, Computer Science, or a related field.
• Post graduate qualifications in data science, statistics, machine learning, data engineering, computer science, or related fields.
• Minimum of 8 years’ experience in data science, machine learning, AI.
• Deep experience with the core tools of Python and SQL.
• Experience with data science open source tools (e.g. MLflow, scikit-learn, Git, Airflow, Postgres, etc.)
• Valid driver’s license and access to own vehicle.