Data Scientist - Python- Banking
VAM SystemsQatar11 days ago
Full-timeEngineering, Information Technology
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Job Description
We are currently looking Data Scientist for our Qatar operations with the following terms & conditions.
Core Responsibilities:
Design, build, and optimize predictive models and machine learning algorithms using structured and semi-structured data.
Perform data pre-processing, feature engineering, and model selection independently.
Build and maintain automated model pipelines including training, validation, scoring and monitoring.
Implement model drift detection, retraining logic, and performance diagnostics.
Conduct code-based model explainability (eg. SHAP, LIME), support documentation for governance review.
Expertise Required
Advanced proficiency in Python (Pandas, NumPy, Scikit-leam, XGBoost, LightGBM)
Strong command of SQL arid handling large datasets (via warehouse or lake)
Experience deploying models using MLflow, Airflow, Docker, or similar tools
Familiarity with model performance metrics (ROC AUC, F1, lift/gain, etc.)
Hands-on in training and evaluating models for binary classification, multi-class, regression, or time series
Exposure to deep learning (PyTorch or Tensorflow) for advanced use cases
Joining time frame: 2 weeks (maximum 1 month)
We are currently looking Data Scientist for our Qatar operations with the following terms & conditions.
Core Responsibilities:
Design, build, and optimize predictive models and machine learning algorithms using structured and semi-structured data.
Perform data pre-processing, feature engineering, and model selection independently.
Build and maintain automated model pipelines including training, validation, scoring and monitoring.
Implement model drift detection, retraining logic, and performance diagnostics.
Conduct code-based model explainability (eg. SHAP, LIME), support documentation for governance review.
Expertise Required
Advanced proficiency in Python (Pandas, NumPy, Scikit-leam, XGBoost, LightGBM)
Strong command of SQL arid handling large datasets (via warehouse or lake)
Experience deploying models using MLflow, Airflow, Docker, or similar tools
Familiarity with model performance metrics (ROC AUC, F1, lift/gain, etc.)
Hands-on in training and evaluating models for binary classification, multi-class, regression, or time series
Exposure to deep learning (PyTorch or Tensorflow) for advanced use cases
Joining time frame: 2 weeks (maximum 1 month)
Key Skills
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