Data Scientist - Python- Banking

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.

• Advanced proficiency in Python (Pandas, Num Py, Scikit-leam, XGBoost, Light
  
  GBM)
• 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)