Machine Learning Engineer

Job description:

• 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 to find solutions

• Optimization and experimentation of outcomes with multiple controllable parameters, including use of simulations, approximations and assumptions as appropriate