Data Scientist – Python, Dataiku & Azure Databricks | UAE

Location:

Dubai, United Arab Emirates

Company / Payroll:
Marc Ellis

Contract Duration: 6 Months, Extendable

Employment Arrangement:
Long-term opportunity under Marc Ellis payroll

Job Category:
Information Technology (IT) & Software;
Science & Research;
Banking & Insurance

Marc Ellis is hiring an experienced Data Scientist for a 6-month extendable contract in Dubai, UAE, with a long-term employment opportunity under Marc Ellis payroll.

The ideal candidate should have strong Python programming expertise and hands‑on production experience with Dataiku, Azure Databricks, PySpark, and MLflow. The role requires advanced knowledge of machine learning, statistical modelling, predictive analytics, feature engineering, and model deployment.

Candidates should have practical experience developing and deploying machine learning models across multiple use cases, including regression, classification, clustering, forecasting, recommendation systems, customer segmentation, risk analysis, and fraud detection.

Experience in NLP, text analytics, sentiment analysis, Generative AI, and production‑grade MLOps practices is also required.

• Perform data preparation, feature engineering, and model development using Python and Dataiku.
• Develop machine learning models using Scikit‑learn, XGBoost, Tensor Flow, and PyTorch.
• Work with Azure Databricks and PySpark for large‑scale distributed data processing.
• Develop and deploy predictive analytics and statistical models.
• Work with structured and unstructured datasets using SQL.
• Develop solutions for regression, classification, clustering, forecasting, and recommendation systems.
• Build models for customer segmentation, risk analysis, and fraud detection.
• Implement NLP, text analytics, sentiment analysis, and Generative AI solutions.
• Deploy and manage machine learning models in production using MLflow, Databricks Model Serving, and Azure ML.
• Implement MLOps workflows covering model versioning, experiment tracking, and model monitoring.
• Support automated retraining, drift detection, performance optimization, and model governance.

1. Python Programming & Machine Learning

• Strong programming expertise in Python.
• Experience with:
  • Pandas.
  • Num Py.
  • Scikit‑learn.
  • XGBoost.
  • Tensor Flow.
  • PyTorch.

2. Dataiku Experience

• Hands‑on production experience with Dataiku.
• Experience in data preparation and feature engineering.
• Model development and deployment.
• Workflow automation and monitoring.

3. Azure Databricks & Big Data

• Experience with Azure Databricks.
• Strong knowledge of PySpark.
• Experience with MLflow.
• Large‑scale distributed data processing.
• Strong SQL skills.
• Experience working with large‑scale structured and unstructured datasets.

4. Machine Learning & Statistical Modelling

Deep understanding of:

• Machine Learning.
• Statistical Modeling.
• Predictive Analytics.
• Data Mining.
• Feature Engineering.
• Model Validation.
• Hyperparameter Optimization.
• Model Explainability.

5. Model Development Experience

Experience developing and deploying:

• Regression models.
• Classification models.
• Clustering models.
• Forecasting models.
• Recommendation systems.
• Customer Segmentation models.
• Risk models.
• Fraud Detection models.

6. NLP & Generative AI

• Natural Language Processing (NLP).
• Text Analytics.
• Sentiment Analysis.
• Generative AI.

7. Production Deployment & MLOps

• Production deployment using MLflow, Databricks Model Serving, and Azure ML.
• CI/CD pipelines.
• Model versioning.
• Experiment tracking.
• Model registry.
• Automated retraining.
• Model monitoring.
• Drift detection.
• Performance optimization.
• Model governance.

Salary and additional employment benefits have not been disclosed.

Contract Duration: 6 months, extendable.

Employment Arrangement:
Long‑term opportunity under Marc Ellis payroll.

The role offers exposure to enterprise‑level data science, distributed data processing, advanced machine learning, Generative AI, and production MLOps environments.