7 of 7 Remote Random Forest Jobs

understanding of computer science fundamentals, including data structures, algorithms, data modelling, and software architecture Solid understanding of classical Machine Learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost, etc.), state-of-the-art research areas (e.g., NLP, Transfer Learning, etc.), and modern Deep Learning algorithms (e.g., BERT, LSTM, etc. More ❯

with some complexity • Utilize traditional and machine learning techniques and tools to build a variety of models, including but not limited to logistic regression, random forest, XGBoost, neural networks, NLP, k-means clustering, ARIMA, and prophet forecasting. • Analyze and interpret results with some complexity • Exercise limited judgment and More ❯

such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials. Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling. Excellent More ❯

Skills and Experience Expert level knowledge of data science and machine learning, including a range of different techniques such as supervised (e.g. decision trees, random forests), unsupervised (e.g. clustering), and deep learning. Knowledge of generative AI is desirable. Expert level of knowledge of statistics, applied mathematics and scientific analysis More ❯

experience working in a relevant field, with experience in some or all of the following: Statistical and data-sampling techniques such as regression, imputation, random forest, Monte Carlo, stratification, and/or clustering; Working with temporal and spatial data; Experience coding in R; Strong scientific writing, report creation More ❯

be able to apply models to datasets to generate inferences in one of the next areas: Computer vision (like yolo) or linear regression/Random Forest. (Desirable) Knowledge and experience with LLMs, LMMs APIs (like GPT4, GPT4V, Llava, Llama or Falcon) (Desirable) Knowledge of AI wrappers like Langchain, OLlama More ❯

estimation/tracking algorithms e.g. Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation methods, Multi-Object-Multi-Sensor Fusion, data-association, random finite sets, Bayesian belief networks, Dempster-Shafer theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent … variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification, deep embeddings, recurrent Networks, natural language processing Computer Vision algorithms e.g. Structure from motion, image Based navigation More ❯