Data Scientist (SQL, Python, Big Query) - Johannesburg - up to R1mil per annum

As a Data Scientist, you will be responsible for designing, developing, and deploying machine learning and optimisation models that operate at enterprise scale. You will work closely with engineering teams, business stakeholders, and clients to transform complex operational challenges into data-driven solutions. This role offers exposure to advanced optimisation problems, geospatial analytics, machine learning, cloud technologies, and enterprise-scale data platforms.

Responsibilities:

• Lead end-to-end machine learning and optimisation projects, from problem definition and prototyping through to deployment and monitoring.
• Develop and enhance solutions supporting territory planning, route planning, and route optimisation services.
• Translate business requirements into technical specifications and implementation plans.
• Design and maintain reporting solutions using Looker Studio, BigQuery, and analytical data models.
• Deliver strategic analytics and consulting projects, providing data-driven recommendations to enterprise clients.
• Collaborate with engineering teams to productionise models through data pipelines, model serving, CI/CD, monitoring, and retraining processes.
• Maintain high standards of code quality, testing, documentation, and reproducibility.
• Mentor junior team members and support external contractors where required.
• Present findings and recommendations to clients and stakeholders.
• Drive continuous improvement in data quality, model performance, and delivery standards.

• Bachelor's Degree in Data Science, Computer Science, Statistics, Mathematics, or a related field, with 5+ years of commercial Data Science experience.
• Strong Python programming skills with hands-on experience in Machine Learning, Data Science Modelling, and predictive analytics.
• Advanced SQL expertise and experience working with large-scale analytical databases, preferably BigQuery.
• Experience developing scalable algorithms, optimisation models, and Operations Research solutions using frameworks such as OR-Tools or PuLP.
• Knowledge of MLOps practices and tools such as MLflow, Vertex AI, SageMaker, Azure ML, CI/CD pipelines, and Git.
• Experience with cloud platforms including Google Cloud Platform (preferred), AWS, or Azure for data science and ML workloads.
• Exposure to Docker, Kubernetes, Looker Studio, geospatial analytics, and software engineering best practices including testing, documentation, and code reviews.