Machine Learning Engineer / Data Scientist

About the Role
We are seeking a Machine Learning Engineer / Data Scientist to develop, validate, deploy, and monitor machine learning models for business and operational use cases. You will build reproducible ML pipelines and operationalize models using enterprise data platforms, delivering end-to-end ML solutions in complex enterprise environments. The ideal candidate has strong applied data science skills and hands-on production ML Ops experience.

Key Responsibilities:

• Translate analytical requirements into predictive and optimization solutions.
• Perform data preparation and feature engineering.
• Train, test, validate, and explain ML models.
• Build pipelines for model versioning, deployment, retraining, and monitoring.
• Integrate models into business and operational workflows.
• Prepare technical documentation and knowledge-transfer materials.

Must Have:

• Experience: Minimum 6 years of data science or machine learning experience, including at least 3 years of hands-on model development and production deployment.
• Production track record: Experience delivering end-to-end ML solutions in complex enterprise environments.
• Python: Advanced Python skills, including pandas, NumPy, scikit-learn, and PyTorch or TensorFlow.
• ML techniques: Strong knowledge of regression, classification, clustering, time-series forecasting, anomaly detection, optimization, and model explainability.
• Azure ML: Hands-on experience with Azure Machine Learning, notebooks, experiments, pipelines, model registry, managed endpoints, and monitoring.
• SQL & data: Strong SQL skills and experience with large-scale structured and time-series datasets.
• ML Ops: Experience with ML flow, Git, Azure DevOps, CI/CD, Docker, APIs, and production ML Ops.
• Language: English required; Arabic speaking preferred.
• Availability: Onsite in Abu Dhabi; able to start as soon as possible.

Good to Have:

• Experience with Microsoft Fabric, Spark, or Databricks.
• Relevant Azure Data Scientist or AI certifications.
• Experience with operational, energy, financial, asset-performance, forecasting, optimization, or predictive-maintenance use cases.