Machine Learning, Python

Key Responsibilities:

• Design, build, and deploy machine learning models for classification, regression, clustering, recommendation, or NLP use cases.
• Develop and maintain data pipelines and feature engineering workflows using Python and associated libraries.
• Use frameworks such as scikit-learn, TensorFlow, PyTorch, or XGBoost to build and train models.
• Conduct model validation, hyperparameter tuning, and performance evaluation (accuracy, recall, F1, ROC, etc.).
• Collaborate with data engineers to handle large datasets using tools like Pandas, NumPy, SQL, and Spark.
• Deploy models to production using APIs, containers (Docker), or cloud platforms (AWS/GCP/Azure).
• Monitor, maintain, and retrain models as needed to ensure reliability and accuracy.
• Document your research, code, and experiments clearly and reproducibly.

Mandatory Skills:

• Proficiency in Python and libraries such as scikit-learn, Pandas, NumPy, Matplotlib, etc.
• Strong foundation in machine learning algorithms and statistical methods
• Experience with model development, training, and evaluation pipelines
• Understanding of data preprocessing, feature engineering, and data wrangling