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
Working knowledge of embeddings, vector stores, or text-based models
Git-based versioning and reproducible ML workflow setup
Joining time frame:
2 weeks (maximum 1 month)
Additional Information
Terms and conditions:
Joining time frame:
maximum 4 weeks
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