5 XGBoost Jobs

or highly transferable skill analogues Experience developing and evaluating models using at least one of the following technologies: —- Gradient-boosted decision trees (e.g. Catboost, XGBoost) —- Reinforcement learning —- Recommendation systems —- Large language models Able to train, tune and evaluate models and prepare them for production deployment Understanding and experience with modern more »

or Azure) Strong proficiency in NumPy for numerical computing and data manipulation tasks. Proven knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Bachelor more »

track record of timely project delivery. Strong programming, statistics, mathematics, and ML algorithm fundamentals, with Python expertise. Familiarity with ML/DS frameworks (e.g., XGBoost, Scikit-learn) and modern OOP practices. Proficient in development best practices and version control (e.g., Git). Knowledgeable in memory, disk I/O, and more »

tree-based methods, optimizers, super/unsupervised learning, feature engineering, etc.). Strong experience with Python required understanding of the ML/DS frameworks (XGBoost, Scikit-learn, Pandas, Numpy, etc.), and modular/modern OOP software design practices with a modern ML/Data/Cloud engineering technical stack If more »

use of AI, ensuring compliance with internal policies and external regulations. Deep understanding of traditional AI techniques: regression, classification, forecasting, clustering (e.g., Decision Trees, XGBoost, ARIMA, etc.) Strong understanding of programmatic LLM risk controls: output format validation, output context validation, content moderation etc. There is a lot more nuance to more »