4 of 4 Linear Regression Jobs in London

Machine Learning Consultant

Hiring Organisation
Quant Capital
Location
London, United Kingdom
Salary
£ 70 K
more than 2 yearsAbility to client face and consultIdeally a PhD masters okStrong understanding of probability and statisticsMachine learning (experience of techniques such as linear regression, logistic regression, boosting, kNN)PythonExperience of one or more common frameworks (e.g. Caffe, Theano, Torch, Tensorflow, scikit-learn)The environment ...

Lead Data Science Consultant

Hiring Organisation
Quant Capital
Location
London, United Kingdom
Salary
£ 70 K
more than 2 yearsAbility to client face and consultStrong Data Science backgroundStrong understanding of probability and statisticsIdeally Machine learning (experience of techniques such as linear regression, logistic regression, boosting, kNN)Ideally Python or another languageExperience of one or more common frameworks (e.g. Caffe, Theano, Torch, Tensorflow, scikit ...

Lead Data Science Consultant

Hiring Organisation
Quant Capital
Location
London, UK
Employment Type
Full-time
more than 2 yearsAbility to client face and consultStrong Data Science backgroundStrong understanding of probability and statisticsIdeally Machine learning (experience of techniques such as linear regression, logistic regression, boosting, kNN)Ideally Python or another languageExperience of one or more common frameworks (e.g. Caffe, Theano, Torch, Tensorflow, scikit ...

Data Science Engineer

Location
London, United Kingdom
which models they chose, the trade-offs, and how they measured success in production Key responsibilities Design, build and validate classical ML models (e.g. regression, tree-based ensembles, clustering) to solve real business problems Select appropriate algorithms and clearly justify model choices to technical and non-technical stakeholders Develop … scientists and product teams to take models from prototype to production Essential skills & experience Classical/traditional ML: Proven experience with models such as linear and logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), SVMs and clustering. You can explain which models you've used ...