Bayesian Modeling Jobs in Texas (NOW HIRING)

What You'll Do 1. Bayesian Machine Learning Models AND Deep learning models to define various allocation optimization, price optimization or fulfillment optimization policies; 2. Time Series Modeling ...

This position will involve training and fine-tuning LLMs and GeoAI models to analyze geospatial ... Bayesian) spatial statistics, spatial accessibility, and spatial optimization • Knowledge of ...

... modeling, inference, weighting, and simulation techniques (e.g. Monte Carlo methods) to understand and estimate variation and uncertainty. Experience with statistical methods such as Bayesian methods ...

... modeling, inference, weighting, and simulation techniques (e.g. Monte Carlo methods) to understand and estimate variation and uncertainty. Experience with statistical methods such as Bayesian methods ...

... Bayesian Networks, Clustering Algorithms, Dimensionality Reduction Techniques. • Experience developing sequence models using RNNs, LSTMs, and Transformers. • Hands-on experience with TensorFlow ...

Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis ...

... model quality sign-off in a cross-functional launch process Preferred Qualifications PhD in Computer Science, Data Science, Statistics, AI/ML, or a related field. Experience with Bayesian or causal ...

Develop and productionalize ML models for high-impact marketing use cases: LTV (Lifetime Value ... Bayesian A/B testing and causal inference to measure the true uplift of AI interventions. • ...

This position will involve training and fine-tuning LLMs and GeoAI models to analyze geospatial ... Knowledge of (Bayesian) spatial statistics, spatial accessibility, and spatial optimization ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... models are tested, monitored, and maintained post-launch Preferred Qualifications PhD in Computer Science, Data Science, Statistics, AI/ML, or a related field. Experience with Bayesian or causal ...

... models are tested, monitored, and maintained post-launch Preferred Qualifications PhD in Computer Science, Data Science, Statistics, AI/ML, or a related field. Experience with Bayesian or causal ...

... Bayesian inference, regression analysis, multivariate methods, experimental design, and ... Ability to explain asymptotic theory, Neyman-Pearson lemma, and generalized linear models while ...

... model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning - Technical skills to consider and apply causal reasoning representation ...

... model, Bayesian network, deep learning, computer vision, NLP/NLU, reinforcement learning, meta-Learning, federated learning - Technical skills to consider and apply causal reasoning representation ...