21 of 21 Permanent Random Forest Jobs in London

behavioural trends.Advanced Analytics: Apply statistical and analytical techniques such as segmentation, clustering, predictive modelling and campaign measurement. Working knowledge of data science techniques including random forest, k-means and linear regression.Optimisation: Collaborate with cross-functional teams to identify opportunities for optimisation. Support initiatives across customer acquisition, retention, loyalty … applying advanced analytical techniques including segmentation, regression analysis, clustering, predictive modelling and campaign measurement. Knowledge of data science and machine learning techniques such as random forest, k-means and linear regression.Strong communication, presentation and data storytelling skills, with the ability to translate complex analytical findings into clear ...

trends. Advanced Analytics: Apply statistical and analytical techniques such as segmentation, clustering, predictive modelling and campaign measurement. Working knowledge of data science techniques including random forest, k-means and linear regression. Optimisation: Collaborate with cross-functional teams to identify opportunities for optimisation. Support initiatives across customer acquisition, retention … applying advanced analytical techniques including segmentation, regression analysis, clustering, predictive modelling and campaign measurement. Knowledge of data science and machine learning techniques such as random forest, k-means and linear regression. Strong communication, presentation and data storytelling skills, with the ability to translate complex analytical findings into clear ...

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 ...

plotly-dash, D3.js, or QlikSenseExperience in SQL (any variant), MS Excel and VBAExperience in applying various analytical techniques (e.g. na ve 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 ...

LangChain, or similar tools.Experience with advanced algorithms in deep learning, neural networks, reinforcement learning, and transfer learning.Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing.Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment ...

similar tools. Experience with advanced algorithms in deep learning, neural networks, reinforcement learning, and transfer learning. Familiarity with traditional machine learning algorithms such as random forests, SVM, logistic regression, and Bayesian modelling for model building, validation, and testing. Familiarity with cloud platforms (e.g., Bedrock, AWS, Azure) for model deployment ...

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 ...

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 ...

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 ...

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 ...

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 ...

Expertise: Solid understanding of computer science fundamentals and time-series forecasting Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning) Machine Learning Engineer, XGBoost, GCP, Python, SQL, time-series forecasting Reasonable Adjustments: Respect and equality ...

Advanced SQL skills Strong data visualisation experience Experience delivering segmentation, clustering, predictive modelling and campaign measurement Understanding of techniques such as k-means clustering, random forest and regression analysis Excellent stakeholder engagement and communication skills Ability to translate analytical findings into business recommendations Previous people management or mentoring ...

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 ...

retention, loyalty, CLV or profitability Comfortable applying advanced analytical techniques such as clustering, regression and predictive modelling Experience with techniques such as k-means, random forest or linear regression would be beneficial Strong commercial and stakeholder management skills, with the ability to explain complex analysis clearly ...

visualisation skills, as well as hands-on experience with advanced techniques such as segmentation, clustering, regression and predictive modelling (for example k-means and random forest). Strong stakeholder communication is essential, as this role involves influencing senior teams across the business. Line management experience would ...

use. We’ll rely on your in-depth knowledge of the ML ecosystem and understanding of varying approaches - whether it’s neural networks, random forests, gradient-boosted trees or sophisticated ensemble methods - to aid decision-making so we apply the right tool for the problem at hand. Your work ...