About the Role :

We are seeking a Data Scientist with hands-on experience in developing, evaluating, and deploying machine learning models and building automated data processing workflows. The candidate will be responsible for translating analytical and business requirements into reliable ML solutions, writing production-quality Python code, and supporting the operationalization of models in enterprise environments.

Key Responsibilities :

- Develop, train, evaluate, and optimize machine learning models for classification, regression, forecasting, and other business use cases.

- Perform data exploration, preprocessing, feature engineering, feature selection, and model validation using structured and unstructured datasets.

- Build end-to-end ML workflows covering data ingestion, transformation, model training, evaluation, and batch or real-time inference.

- Develop modular, reusable, and maintainable Python code using pandas, NumPy, scikit-learn, XGBoost, and TensorFlow/PyTorch.

- Automate model execution, batch scoring, and data processing tasks using Python scripts, scheduled jobs, and workflow orchestration tools.

- Write efficient SQL queries to extract, transform, validate, and analyze data from relational databases and enterprise data platforms.

- Support model deployment and integration with APIs, databases, and downstream applications in collaboration with engineering teams.

- Use MLflow or equivalent tools for experiment tracking, model versioning, and reproducibility.

- Implement unit tests, data validation checks, logging, exception handling, and documentation for ML workflows.

- Monitor deployed models for prediction quality, data quality issues, performance degradation, and operational failures.

- Troubleshoot pipeline failures, investigate model performance issues, and implement corrective actions.

- Follow established standards for code quality, security, model governance, and responsible AI.

Required Skills & Expertise :

- Strong proficiency in Python, object-oriented programming, SQL, and common software engineering practices.

- Hands-on experience developing and evaluating supervised machine learning models.

- Working knowledge of feature engineering, cross-validation, hyperparameter tuning, and model performance metrics.

- Experience with pandas, NumPy, scikit-learn, and at least one gradient-boosting framework such as XGBoost or LightGBM.

- Experience building automated data processing scripts and repeatable ML workflows.

- Familiarity with Git, code reviews, unit testing, and debugging.

- Exposure to MLflow, Airflow, or equivalent tools for experiment tracking and workflow orchestration.

- Understanding of model deployment, batch inference, REST APIs, and basic production monitoring.

- Strong analytical thinking, communication, collaboration, and problem-solving skills.