Data Scientist

Key Responsibilities:
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.