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Data Scientist - Python- Banking at VAM Systems
Doha, Doha, Qatar -
Full Time

Start Date

Immediate

Expiry Date

14 Jun, 26

Salary

0.0

Posted On

16 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, SQL, MLflow, Airflow, Docker, Model Performance Metrics, Binary Classification, Multi-class Classification, Regression, Time Series, PyTorch

Industry

IT Services and IT Consulting

Description
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)
Responsibilities
The role involves designing, building, and optimizing predictive models and machine learning algorithms using various data types, alongside building and maintaining automated model pipelines for training, validation, scoring, and monitoring. Responsibilities also include implementing drift detection, retraining logic, performance diagnostics, and conducting code-based model explainability.