7 of 7 Remote Random Forest Jobs in London

machine learning services) Timeseries forecasting Solid understanding of computer science fundamentals, including data structures, algorithms, data modelling and software architecture Strong knowledge of Machine Learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, etc.) as well as state-of-the-art research area (e.g. NLP, Transfer Learning etc.) and modern Deep Learning algorithms (e.g. BERT, LSTM, etc.) Machine Learning 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 ❯

work with ML Engineers to ensure those are deployed in production and delivering real value. Activities will include: Developing data science and ML solutions (using, e.g., python, SQL, GBM, random forests) to drive growth, solve problems and increase automation across the business. Work within a cross-functional team (product managers, ML engineers, analysts) to deliver business goals. Influence and More ❯

multivariate implementations 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. Familiarity with statistical and analytical methods for genetics and -omics data analysis and working knowledge of high dimensional biomarker platforms (e.g., next generation More ❯

multivariate implementations 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. Familiarity with statistical and analytical methods for genetics and -omics data analysis and working knowledge of high dimensional biomarker platforms (e.g., next generation 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 ❯