10 of 10 Permanent Random Forest Jobs in London

skills using tools like Tableau, Spotfire, Power BI etc. 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 ...

experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time series forecasting. Working understanding of model evaluation and explainability concepts, e.g. overfitting, bias, cross-validation. Experience ...

dash, D3.js, or QlikSense Experience in SQL (any variant), MS Excel and VBA Experience in applying various analytical techniques (e.g. naïve bayes, decision trees, random forest, xgboost and k-means), to calculate problems such as predictive forecasting, prescriptive action, capacity planning. Experience of working in teams, managing ...

data, analytics and automation Experience integrating REST APIs and multiple data sources Knowledge of traditional machine learning techniques, including tree-based models (e.g. Random Forest, CatBoost), feature engineering techniques such as TF-IDF, and experience evaluating and deploying AI/ML solutions in commercial environments Excellent problem-solving ...

developing cloud-based machine learning services using one or more cloud providers (preferably GCP) Excellent understanding of classical Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) < Strong knowledge of SQL and Python's ecosystem for data analysis ...

quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command ...

quantitative discipline such as Computer Science, Statistics, Mathematics, Economics, or similar Working knowledge of statistical and machine learning methods (e.g. logistic regression, gradient boosting, random forests, clustering, NLP Proficient in Python and/or R, with strong experience in data quality, model development and feature engineering Strong command ...

partnerships Apply traditional model development techniques and approaches (e.g. logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc. Utilise the latest cloud based technologies and analytical tooling to support test-and-learn champion/challenger approaches towards new fraud ...

from partnershipsApply traditional model development techniques and approaches (e.g. logistic regression) as well as adopting new AI/ML techniques such as gradient boosting, random forests, clustering, etc.Utilise the latest cloud based technologies and analytical tooling to support test-and-learn champion/challenger approaches towards new fraud scores ...

Experience in financial markets, algo-trading, or prediction market platforms (Polymarket, Manifold, etc.) Quantitative background in maths, statistics, or probability theory ML experience including random forests, regression models, scikit-learn, and PyTorch AWS infrastructure experience deploying containerised applications and managing cloud environments (EC2, Lambda, RDS) Open-source contributions ...