DATA SCIENTIST @ NAIR SYSTEMS
Nair Systems is seeking a highly skilled Data Scientist to join our operations in Qatar. The successful candidate will be responsible for designing, building, and optimizing advanced predictive models and machine learning algorithms using both structured and semi-structured data. You will take ownership of data pre-processing, feature engineering, and model selection, ensuring that automated pipelines for training, validation, and monitoring are robust and scalable.
In addition to core modeling tasks, you will be expected to implement sophisticated diagnostics, including model drift detection and retraining logic. You will play a key role in model governance by providing code-based explainability through methods like SHAP or LIME and supporting documentation for reviews. This role requires an immediate start and offers the opportunity to work with cutting-edge technologies like vector stores and deep learning frameworks in a production environment.
In addition to core modeling tasks, you will be expected to implement sophisticated diagnostics, including model drift detection and retraining logic. You will play a key role in model governance by providing code-based explainability through methods like SHAP or LIME and supporting documentation for reviews. This role requires an immediate start and offers the opportunity to work with cutting-edge technologies like vector stores and deep learning frameworks in a production environment.
Key Requirements
Advanced proficiency in Python, specifically with Pandas, NumPy, Scikit-learn, XGBoost, and LightGBM.
Strong command of SQL and experience handling large datasets via data warehouses or lakes.
Experience deploying models using tools such as MLflow, Airflow, or Docker.
Familiarity with model performance metrics including ROC AUC, F1 scores, and lift/gain analysis.
Hands-on experience training and evaluating models for binary classification and regression.
Exposure to deep learning frameworks such as PyTorch or TensorFlow for advanced use cases.
Working knowledge of embeddings, vector stores, or text-based models.
Proficiency in Git-based versioning and establishing reproducible ML workflows.
Ability to perform data pre-processing and feature engineering independently.
Competency in implementing model drift detection, retraining logic, and performance diagnostics.