The Senior Data Scientist will lead the design, development, and deployment of advanced analytics and machine learning solutions that drive strategic decision-making and operational efficiency.

This role requires a deep understanding of data science, data engineering, and AI concepts, and will play a pivotal role in embedding intelligent automation and predictive modelling across the organisation.

Responsibilities

• Build and implement machine learning models using structured and unstructured data to improve forecasting accuracy and enable proactive decision-making.
  
• Optimise model performance and scalability through hyper parameter tuning and algorithm selection to enhance efficiency and reduce computational costs.
  
• Implement reproducible research practices by using version control, documentation, and testing to maintain model integrity and facilitate collaboration.
  
• Monitor deployed models in production using performance metrics and alerting systems to ensure reliability and timely intervention.
  
• Automate repetitive data science tasks through scripting and workflow orchestration to increase productivity and reduce manual errors.
  
• Maintain high data quality standards by conducting regular audits and validation checks to support trustworthy analytics.
  

Machine Learning

• Expert in designing, developing, and deploying advanced machine learning and AI models.
  
• Expert in selecting appropriate algorithms, optimising model performance, and mentoring junior team members in best practices.
  

Data Engineering & Architecture

• Understanding of ETL/ELT processes and data pipeline design.
  
• Ability to collaborate with data engineers to ensure data quality and accessibility.
  

Requirements

Qualifications

• Matric and a Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
  
• 10 years of experience in data science, with at least 2–3 years in a senior or lead role.
  
• Proven experience in developing and deploying machine learning models in production environments.
  
• Strong proficiency in Python, R, SQL, and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
  
• Solid understanding of data engineering principles and cloud data architectures (e.g., Azure, AWS, GCP).
  
• Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow).
  
• Excellent communication and stakeholder engagement skills.
  

Advantageous

• Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
  
• Experience with large language models (LLMs) and generative AI.
  
• Experience in healthcare, retail, or insurance data ecosystem