Data science Requirements: •
- Ability to understand and solve Data Quality Issues Having previously worked on building models related to recommender systems, Targeted Marketing campaign models for win-back campaigns
- Have an understanding of Model deployment Life Cycle and data engineering Have an understanding of building model re-training pipelines for Model and data versioning along with incorporating test cases within the ML pipeline (In-variance Testing, Pre-model testing, and Post model testing)
- Strong exposure to Machine Learning Algorithm usage and hyper-parameter tuning to optimize the model for deploying to production environments (GridSearch, K-fold Cross Validation)
- Have an understanding of real-time data retrieval for In-app events data analysis.
EXPERTISE AND QUALIFICATIONS
• Have a very good working knowledge of Python and SQL.
• Good to have an understanding of Git-related concepts, Github actions
• Good to have AWS components knowledge related to data engineering and model deployment
• Hand-on experience in working on ML techniques such as clustering techniques, classification models, propensity models