Azure data scientist
Summary
Builds and deploys ML models on Azure, using Python and Azure ML tooling to deliver data-driven insights and automated workflows for business stakeholders.
The Azure Data Scientist will be responsible for developing machine learning models, applying advanced analytics techniques, and delivering data-driven insights using Azure-based tooling. This role requires strong analytical capability, statistical modelling expertise, and hands‑on experience with Azure ML ecosystems.
Key Responsibilities Build, train, validate, and deploy machine learning models on Azure (Azure ML, Databricks). Perform data exploration, feature engineering, statistical modelling, and predictive analytics. Collaborate with data engineers to ensure availability of high‑quality training data. Work with business stakeholders to translate business problems into ML solutions. Develop automated ML workflows using Azure ML Pipelines, MLOps practices, and versioning strategies. Monitor model performance, drift, and quality; implement continuous improvement processes. Present insights, findings, and recommendations to technical and non-technical audiences. Required Technical Skills Proficiency in Python (pandas, scikit‑learn, Num Py, Py Torch/Tensor Flow optional). Experience with Azure Machine Learning, Databricks, Synapse, and associated ML tooling. Strong statistical and mathematical modelling skills. Experience with MLOps, Azure Dev Ops, Git version control, and CI/CD for ML. Familiarity with distributed ML and big data frameworks (Spark ML). Ability to work with structured and unstructured datasets. Qualifications & Certifications (Preferred) DP‑100 Azure Data Scientist Associate. Other relevant Azure or ML certifications (Databricks ML, Tensor Flow Developer, etc.). Degree in Data Science, Applied Mathematics, Computer Science, Statistics, or equivalent. Experience 3–5+ years in machine learning, AI, data science, or advanced analytics. Proven experience delivering production‑ready ML models in cloud environments. Location & Working Model Location: Johannesburg (Hybrid working model) Ways of Working: Standard SAST business hours Candidate: Preferably a South African Citizen or Permanent Resident.