16 of 16 Permanent Random Forest Jobs in London

and manage deliverables Qualifications Solid 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.) Solid knowledge of SQL and More ❯

Gen AI and machine learning. Experience analyzing large data sets, data cleaning, and statistical analysis. Proven experience with at least three machine learning algorithms (e.g., neural networks, logistic regression, random forests). Proficiency with Java and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow or PyTorch. Experience with More ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer Science More ❯

Skills and Experience: Previous experience within Personal Lines Pricing is advantageous 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 good quantitative degree (Mathematics, Statistics, Engineering, Physics, Computer Science More ❯

/or statistical packages e.g. actuarial pricing software Experience in SOME of the following predictive modelling techniques e.g.Logistic Regression, Log-Gamma GLMs, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Support Vector Machines and Neural Nets Experienced in the use of a programming language (e.g. R, Matlab, Python or Octave) Experience of using Emblem and Radar Highly numerate More ❯

ongoing don't miss your chance to secure the future of your career! Contact me @ (url removed) or on (phone number removed). Data Science, Data Scientist, AI, ML, Random Forest, Databricks, SageMaker, Regression, Gradient Boosting, NLP, Palantir, Insurance, Banking More ❯

and understanding of statistical methods 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 interpersonal and communication skills (verbal More ❯

Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural More ❯

Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural More ❯

Manage the full model development lifecycle, including data preparation, exploratory analysis, model training, validation, and deployment. Develop and fine-tune predictive algorithms such as: Classification: Logistic Regression, Decision Trees, Random Forests. Regression: Linear Models, Gradient Boosting, Neural Networks, K-Nearest Neighbors. Build models for customer behavior prediction and risk assessment in insurance, particularly in underwriting and claims. Apply natural More ❯

discipline such as Computer Science, Statistics, Operational Research or Engineering • Experience working in Analytics/Business Intelligence environment • Experience/knowledge of advanced machine learning techniques such as GBM, random forest, etc. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during More ❯

and understanding of statistical methods 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 interpersonal and communication skills (verbal More ❯

of applying NLP expertise is of crucial importance to this hire. Head of Data Science : - Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, Random Forests, etc. - Experience with common data science toolkits, such as Python - Proficiency in using query languages such as SQL on a big data platform e.g. Hadoop, Hive - Good applied More ❯

the risk models and working closely with underwriting and technical modelling teams. ✔️ Personal Lines Pricing experience (Motor, Home or Pet) ✔️ Strong skills in predictive modelling – e.g., GLMs, GBMs, GAMs, Random Forests ✔️ Proficiency in R, Python, PySpark, SAS or SQL ✔️ Experience with Radar and/or Emblem ✔️ A numerate degree (e.g., Maths, Stats, Actuarial, Engineering) ✔️ Strong communication and stakeholder management More ❯

the risk models and working closely with underwriting and technical modelling teams. ✔️ Personal Lines Pricing experience (Motor, Home or Pet) ✔️ Strong skills in predictive modelling – e.g., GLMs, GBMs, GAMs, Random Forests ✔️ Proficiency in R, Python, PySpark, SAS or SQL ✔️ Experience with Radar and/or Emblem ✔️ A numerate degree (e.g., Maths, Stats, Actuarial, Engineering) ✔️ Strong communication and stakeholder management More ❯

of mathematical competence. The ability to code or have programming experience, especially in Python. Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, Natural Language Processing, optimisation). Experience with some coding libraries frequently used in data science. The ability to communicate effectively. Experience composing and following a project More ❯