6 of 6 Permanent Random Forest Jobs in London

manage deliverables What we offer Solid understanding of computer science fundamentals, 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 areas (e.g. NLP, Transfer Learning) and modern Deep Learning algorithms (e.g. BERT, LSTM) Solid knowledge of SQL and Python's ecosystem More ❯

and experience in exploratory data analysis, inferential statistics, and machine learning, including: Clustering techniques (e.g., k-medoids, hierarchical clustering) Predictive modelling (e.g. Classification and Regression Trees (CART), Linear Regression, Random Forest, Gradient Boosted Models) Natural Language Processing (NLP) with a focus on social listening and topic modelling Integration of generative AI and LLMs in qualitative and survey research More ❯

processing of and cleaning of data, merging/joining disparate data sources, feature engineering, performing analyses and communicating results). Produce models using a variety of algorithms (GLM, GBM, Random Forest, Neural Networks etc.) and assess the relative strength of each model Identify which factors are relevant and predictive and should be included in the model build Document More ❯

for clients. Create segmentation models using multinomial logistic regression and linear discriminant analysis. Advanced Analytics Skills Strong working knowledge of analytical techniques such as conjoint analysis, machine learning (e.g., Random Forests, SVM), statistical methods (e.g., regression), time series, basket analysis, and unstructured data analytics. Ability to synthesize multiple data sources into meaningful insights and actionable business metrics. Knowledge of More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯

and/or specialized courses are an asset, especially in Data Science, Quantitative Finance or similar. Should desirably have knowledge of modeling techniques (logit, GLM, time series, decision trees, random forests, clustering), statistical programming languages (SAS, R, Python, Matlab) and big data tools and platforms (Hadoop, Hive, etc.). Solid academic record. Strong computer skills. Knowledge of other languages More ❯