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Azure Data Scientist

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Job Description - Azure Data Scientist

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


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