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, NumPy, PyTorch/TensorFlow optional).
• 
  Experience with
  Azure Machine Learning
  ,
  Databricks
  ,
  Synapse
  , and associated ML tooling.
• 
  Strong statistical and mathematical modelling skills.
• 
  Experience with
  MLOps
  , Azure DevOps, 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.

Requirements

Qualifications & Certifications (Preferred)

• 
  DP‑100 Azure Data Scientist Associate
• 
  Other relevant Azure or ML certifications (Databricks ML, TensorFlow 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