- Review available datasets, identify data-quality issues, and prepare data for analysis and modelling.
- Explore patterns, assess helpful features, and select appropriate preprocessing methods.
- Choose and compare suitable statistical and machine-learning models based on the problem, dataset size, and intended use.
- Train models, tune their settings, and select prediction thresholds where appropriate.
- Design suitable training, validation, and testing approaches, avoiding data leakage and overfitting.
Must-have: Practical Python experience, hands-on predictive modelling and understanding of model evaluation.
Essential Skills
Exp on developing ML-based prediction models
Exp on using Python for developing ML models Which libraries
Exp on using on classification or regression models
Exp on using testing and checking the accuracy of ML models
Exp on working with different ML algorithms and compared their results
Exp on integrating ML models into Python applications
Exp on working with CSV/Excel datasets for data preparation and ML modelling
Hands-on experience writing SQL queries and an understanding of database normalization
📌 Data Scientist (Bengaluru)
🏢 Digile
📍 Bengaluru
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