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Make sure to read the full description below, and please apply immediately if you are confident you meet all the requirements.- Supervised machine learning techniques, including ensemble tree and other non parametric techniques for prediction of binary and continuous outcomes, e.g. xgboost, LightGBM, CatBoost, deep learning
- Feature engineering, including feature ideation, and creation via transformations and aggregations of raw data
- Model development process, including train/test, k-fold CV, appropriate performance metrics
- Unsupervised methods for clustering and segmentation of consumers, including k-means, k-mode, DBSCAN
- Experience translating business problems into supervised and unsupervised machine learning problems xcfaprz to find solutions
- Optimization and experimentation of outcomes with multiple controllable parameters, including use of simulations, approximations and assumptions as appropriate
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Languages
- English
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