Data Scientist - AI Applications

About the Role

Our client is looking for a skilled Data Scientist with a focus on AI Applications to join their dynamic team in Richards Bay . In this role, you will leverage advanced statistical and machine learning techniques to extract insights from complex datasets and develop innovative AI-driven solutions. You will play a key part in analyzing data, building predictive models, and communicating findings to stakeholders to inform strategic decisions. This is an exciting opportunity to contribute to impactful projects within the AI and Emerging Technologies sector, working in a collaborative environment that encourages continuous learning and exploration.

Key Responsibilities

• Develop and implement machine learning models for predictive analytics, classification, and pattern recognition.
• Clean, transform, and analyze large, complex datasets to identify trends and insights.
• Design and conduct experiments to test hypotheses and validate model performance.
• Visualize data and model results, creating clear and compelling reports for technical and non-technical audiences.
• Collaborate with engineers to integrate models into production systems and applications.
• Stay current with state-of-the-art data science and AI techniques.

Requirements

• Master's or PhD degree in Data Science, Statistics, Computer Science, or a related quantitative field.
• Proven experience (4+ years) as a Data Scientist with a strong focus on AI/ML.
• Proficiency in programming languages such as Python or R, and expertise in libraries like pandas, NumPy, and scikit-learn.
• Solid understanding of statistical modeling, hypothesis testing, and experimental design.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).
• Strong problem‑solving abilities and the capacity to work effectively in a hybrid setting.

Benefits

• Competitive salary and annual bonus.
• Comprehensive medical aid and retirement fund contributions.
• Opportunities for professional development and training courses.
• Hybrid work model offering flexibility between office and remote work.
• Collaborative and innovative team culture.

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