3 of 3 Statistical Modelling Jobs in Stevenage

and the Chief Data Office. Key accountabilities. Analysis & investigation: Undertake and guide junior data scientists in various complex data analyses, investigations and/or modelling of business issues to improve the management, services, and products of the bank. Predictive model development: Take a leading role in building predictive models … years' experience in a model monitoring, model development or model validation role. Examples include IRB; IFRS 9; loss forecasting; stress testing or economic capital modelling; propensity modelling; or a combination thereof. A bachelor's degree in a quantitative analytical discipline (2.1 or higher), e.g., mathematics, applied mathematics, physics … equivalent level in an alternate programming language would be consider (e.g., R, Python, Matlab). Advanced experience in extracting, transforming, and cleaning data for modelling purposes. Familiarity with data visualisation tools such as QlikView, Power BI, SAS VA or Tableau. Strong ability to build relationships and communicate with key More ❯

presentational skills. Ability to extract data from multiple sources and collate into meaningful and accurate analysis using recognised software packages. Quantitative Analysis: proficiency in statistical methods and financial modelling to assess and mitigate risks. SQL Proficiency: Ability to write simple database queries and create reports with actionable information. More ❯

a degree with mathematical content and programming skills Relevant experience (Post-Doctoral or industrial) in robotics, data fusion, tracking/estimation, pattern discovery & recognition, statistical inference, optimisation and machine/deep learning algorithms along with real-time implementation, and/or validation & verification is a strong advantage Experience in … theory of evidence Machine Learning for regression and pattern recognition/discovery problems e.g. Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random-forests, novelty detection, clustering Deep Learning e.g. Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification More ❯