- Lead the design, development, and deployment of machine learning and statistical models to solve real-world business problems.

- Collaborate with cross-functional teams (Product, Engineering, Business) to understand business needs, define project scopes, and translate them into data science problems.

- Develop, test, and productionize models using Python, SQL, and PySpark on large-scale datasets.

- Apply deep learning and advanced statistical modeling techniques for use cases such as classification, regression, recommendation, and personalization.

- Perform feature engineering, data wrangling, model selection, hyperparameter tuning, and model evaluation.

- Monitor model performance and ensure robust ML lifecycle management using tools like MLflow and Airflow.

- Document processes and present insights, findings, and recommendations to both technical and non-technical stakeholders.

- Stay current with new trends in ML/AI and proactively apply emerging techniques where appropriate.

Preferred candidate profile :

- 3 years of hands-on experience in data science.

- Strong proficiency in Python and libraries such as NumPy, pandas, scikit-learn, TensorFlow/PyTorch.

- Expertise in SQL for data extraction, transformation, and analysis.

- Proven experience with PySpark or distributed data processing frameworks.

- Deep understanding of machine learning and deep learning algorithms and model development workflows.

- Solid grounding in statistical modeling, hypothesis testing, and data analysis.

- Experience in building and deploying production-grade models at scale.