1 to 9 of 9 Permanent Random Forest Jobs in the UK

transparency in pricing practices. YOUR SKILLS AND EXPERIENCE Strong quantitative degree. Experience in predictive modelling techniques such as logistic regression, GLMs, GBMs, Decision Trees, Random Forests, or Neural Networks Experience in personal lines insurance Strong coding skills with Radar and either Python or SQL Experience with machine learning platforms more »

actionable results Strong PowerPoint and presentation/communication skills Expertise in predictive modelling, including both parametric (e.g. logit/probit) and non-parametric (e.g. random forest, neural net) techniques as well as wider ML techniques like clustering/random forest (desirable) Tech Stack: SQL, Python, R more »

to Machine Learning Engineer, Data Scientist, Data Science, Machine Learning, Python, NLP, Natural Language Processing, Computer Science, Statistics, Data pipelines, SQL, Regression, Clustering, XGBoost, Random Forest, Decision Trees, Predictive modelling, FinTech, ML Ops, Deep Learning, Neural Networks, Deployment, Containerisation, Generative AI, LLMs, large language models Company: Xcede Qualifications more »

A desire and ability to research and pick up new tools and techniques quickly is essential. Some Experience in running various methods like Regression, Random forest, k-NN, k-Means, boosted trees, SVM, Neural Network, text mining, NLP, statistical modelling, data mining, exploratory data analysis, statistics (hypothesis testing more »

techniques such as Multi-Touch Attribution, Marketing Mix Modelling or similar would be highly desirable Knowledge of machine learning techniques such as Neural Nets, Random Forest or XgBoost and working with LLMs and associated techniques would be beneficial You are highly driven, dedicated, and have a keen sense more »

techniques such as Multi-Touch Attribution, Marketing Mix Modelling or similar would be highly desirable Knowledge of machine learning techniques such as Neural Nets, Random Forest or XgBoost and working with LLMs and associated techniques would be beneficial You are highly driven, dedicated, and have a keen sense more »

parameter estimation, factors selection, PCA, hypothesis testing, time series, queuing theory, survival analysis, clustering, linear programming. Experience with machine learning methods, such as regularization, random forests, neural networks and deep learning. Ability to write algorithms and implement pipelines in Python. Knowledge of Scala, R, is a plus. Experienced in more »

SQL across structured, semi-structured and unstructured data. Ability to use dimension reduction techniques (PCA, encoders etc.) Excellent familiarity with elastic net logistic regression, random forest and XGBoost ensembles to work on supervised problems with structured, tabular data. We currently use Scikit-learn, and we're open to more »

Solid understanding of computer science fundamentals, including data structures, algorithms, data modeling, and software architecture. Proficiency in classical machine learning algorithms (e.g., Logistic Regression, Random Forest, XGBoost) and modern deep learning algorithms (e.g., BERT, LSTM). Strong knowledge of SQL and Python's data analysis ecosystem (Jupyter, Pandas more »