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Hybrid Azure Data Scientist na Amidel

Amidel · Johannesburg, África do Sul · Hybrid

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Amidel is an Information Technology and Business Consulting Company that provides highly specialised solutions to large and small enterprises in both the private and public sectors. We seek a highly motivated and experienced Intermediate/Senior Azure Data Scientist. The ideal candidate should have a strong background in machine learning, data engineering, and AI model deployment using Microsoft Azure technologies.

 

This role presents a dynamic opportunity to work on impactful data-driven solutions with leading organisations in sectors such as finance, healthcare, and public service. The candidate should ideally hold the Microsoft Certified: Azure Data Scientist Associate certification.

 

Job Family:

  • Data Science / Artificial Intelligence / Cloud Analytics

Minimum Experience:

  •  5 Years working in data science, analytics, or AI/ML solution development.
  • 3 Years working on Microsoft Azure-based data science platforms.

Qualification:

  •  Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, or a related field
  • Advantageous: Microsoft Certified: Azure AI Engineer Associate or Azure Solutions Architect Expert

Contract Type:

Permanent or Fixed-Term

 

DUTIES AND RESPONSIBILITIES 


Responsibilities vary by client engagement and may include:

  • Designing, building, and deploying scalable machine learning models on Azure
  • Working with Azure Machine Learning (Azure ML) to train, test, and operationalize AI models.
  • Performing data ingestion, transformation, and feature engineering using Azure Data Factory, Synapse Analytics, and Databricks
  • Collaborating with data engineers to develop robust data pipelines and prepare data for modelling
  • Selecting and fine-tuning appropriate ML algorithms and evaluation metrics for different business problems
  • Integrating machine learning models into business applications via APIs or embedded services
  • Enabling secure and reproducible ML workflows using ML pipelines and automated ML (AutoML)
  • Leveraging responsible AI practices to ensure fairness, accountability, and transparency in models.
  • Creating interactive reports and dashboards using Power BI to visualise predictive insights.
  • Working with large datasets from diverse sources (structured, semi-structured, unstructured)
  • Engaging with business stakeholders to translate use cases into AI solutions.
  • Supporting model monitoring, drift detection, and lifecycle management in production environments
  • Documenting processes and sharing knowledge with teams and clients

 

COMPETENCIES

 

  • Proven experience building and deploying machine learning models in cloud environments.
  • Proficiency in Python and common ML/data libraries (e.g., scikit-learn, pandas, NumPy, TensorFlow, PyTorch).
  • Experience using Azure ML, Synapse Analytics, and Data Lake for data science workflows.
  • Knowledge of data pre-processing, feature engineering, and model validation techniques.
  • Familiarity with MLOps principles and automated ML lifecycle management.
  • Strong understanding of statistics, data wrangling, and algorithm optimisation.
  • Experience working with both structured and unstructured data (e.g., text, images, sensor data).
  • Hands-on experience with data integration tools such as Azure Data Factory and Databricks.
  • Microsoft Certified: Azure Data Scientist Associate (must have or in progress).
  • Excellent communication and presentation skills for both technical and non-technical audiences.
  • Ability to work in cross-functional teams and translate complex problems into actionable solutions.
  • Financial or enterprise industry experience is an advantage.
  • Exposure to hybrid cloud data environments is a plus.

 

If you are passionate about solving real-world problems through data, machine learning, and AI innovation on the Microsoft Azure platform, we encourage you to apply. This is a unique opportunity to shape intelligent solutions and accelerate digital transformation across industries while growing your expertise in the Microsoft data ecosystem.

 

 

 

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