7 of 7 Permanent Logistic Regression Jobs in London

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 More ❯

Dataiku, Anaconda, Sagemaker, Vertex etc. AIML Frameworks: MLFlow, KubeFlow, BentoML, TensorFlow etc. AIML Development: PyTorch, Jupyter Notebooks, XGBoost, Tensorflow etc. AIML Models: Linear/Logistic Regression, KNN, Decision Trees, Anomaly Detection, LLMs, Generative Models (PALM, GPT3/4), Entity Extraction etc. Inside IR35 contract, rates to reflect More ❯

and Docker is a positive. An understanding of machine learning processes and their applications to investments. Ideally, they will be familiar with various algorithms (logistic regression, neural networks) and an understanding of how to implement these in Python. A passion for bringing together investment ideas in an organized More ❯

Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience. 5+ years of data scientist experience. Experience with statistical models e.g. multinomial logistic regression. PREFERRED QUALIFICATIONS Experience working with data engineers and business intelligence engineers collaboratively. Experience managing data pipelines. Experience as a leader and mentor on More ❯

the main concepts of Data Science/Machine Learning would be useful. We use a range of predictive analytics and machine learning methodologies, including logistic regression and cluster analysis, plus some predictive time series analysis. We are committed to creating a diverse environment and are proud to be More ❯

the main concepts of Data Science/Machine Learning would be useful. We use a range of predictive analytics and machine learning methodologies, including logistic regression and cluster analysis, plus some predictive time series analysis. More ❯

the main concepts of Data Science/Machine Learning would be useful. We use a range of predictive analytics and machine learning methodologies, including logistic regression and cluster analysis, plus some predictive time series analysis. More ❯