Introduction
Position: Snr Advisor Data Science Task Grade: G16 Area of specialization: To design artificial intelligence (AI), machine learning (ML) and deep learning (DL) solutions for the division by leveraging advanced data analytics to improve business outcomes while deriving value for business. Department: Market Operator Business Unit: Energy Market Services Location: Simmerpan - Germiston, Gauteng
Job Description
Skills and Competencies Required
Behavioural:
Honesty
Integrity
Professionalism
Ethics
Curious
Leadership:
Critical thinking
Sound business and people management acumen
Stakeholder management
Manage vision and purpose
Teamwork
Knowledge:
System, Applications and Products (SAP) Material Requester (MR1)
Computer programming languages (Python, Java)
Understand and demonstrate agile development life cycles
Data sciences specialist topics of artificial intelligence, machine learning and deep learning
Databases and foundation models
Various software extensions, principles and standards
Data modelling and evaluation
Natural language processing
Adobe Creative Suite
Microsoft Suite
Signal processing techniques
Project management
Reinforcement learning
Skill
Audio and video processing
Neural network architecture
Mechanical thinking
Collaborative
Think creatively and innovatively.
Problem solving and decision-making
Communication
Analytical, mathematical, and creative problem-solving
Key Responsibilities
Architect, design, develop and deploy AI/ML/DL solutions.
Build, maintain and improve analytic models, reporting solutions and decision systems.
Innovate and design new machines to learn prototypes and solutions.
Manage the data science portfolio and the advanced analytics roadmap.
Research, upskill and stay abreast of new developments.
Minimum Requirements
Qualification(s):
B Degree/BTech in Computer Science/Mathematics/Statistics/Physics/Engineering/Actuarial Science at NQF7 with 360 credits
Experience
Related 5 years' experience in Data science and analytics including artificial intelligence (AI), machine learning (ML), deep learning (DL)
Deadline:22nd April,2026