17 of 17 Permanent Random Forest Jobs in London

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 ...

techniques, e.g. skilled in linear algebra. Experience conducting hands-on analytics projects using advanced statistical methods such as generalized regression models, Bayesian methods, random forest, gradient boosting, neural networks, machine learning, clustering, or similar methodologies. Proficiency in programming languages such as Python, Sql etc. Experience with Data visualization ...

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 ...

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 ...

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 ...

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 ...

models R&D projects Requirements: Recent graduates or final year students. Should desirably have knowledge of Artificial Intelligence modeling techniques (logit, GLM, time series, random forests, clustering, XGBoost), statistical programming languages (SAS, R, Python, Matlab), big data tools and platforms (Hadoop, Hive, etc.) and cloud platforms (AWS, Azure ...

Previous experience within general insurance pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL) A quantitative degree (Mathematics, Statistics ...

Previous experience within general insurance pricing Experience with some of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in statistical and data science programming languages (e.g. R, Python, PySpark, SAS, SQL) A quantitative degree (Mathematics, Statistics ...

logistic or Poisson, multilevel models, Analysis of Variance. Bayesian approaches to modelling Causal inference Epidemiology Machine Learning/predictive models: regression/classification trees, random forests, boosting, neural networks Experimental design and applying the appropriate testing framework to analytics. Observational study design using real world data Expert in open ...

recommendations Apply statistical and advanced analytical techniques including regression, clustering, predictive modelling and campaign measurement Have an understanding of techniques such as k-means, random forest and linear regression, with the ability to interpret and utilise modelling outputs Support forecasting and optimisation activity across the wider analytics function ...

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 ...

Poisson, multilevel models, Analysis of Variance. o Bayesian approaches to modelling o Causal inference o Machine Learning/predictive models: regression/classification trees, random forests, boosting, neural networks Experimental design and applying the appropriate testing framework to analytics. Experience improving decision quality, working with system leaders ...