Senior Data Scientist Team Lead (Contract)

Introduction 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.

Duties & 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 hyperparameter 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.

Desired Experience & Qualification

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