existing systems, enabling customers to see what's important and change outcomes. Reporting to: Head of Global Business Delivery Key Responsibilities: Work with Commercial Delivery team to: Conduct data analysis based on current deployments and communicate key insights through reports, presentations and visualisations to stakeholders. Manage external and internal video analysis teams, perform moderation of results to ensure … consistency of outputs in order to deliver reporting in accordance with client reporting schedule. Use video analysis outputs to generate regular internal assessments of the solution performance, identify areas of improvement and guide product requirements to achieve a solution performance level which meets customer expectations. Support business scaling as needed: e.g. build internal and external reporting required for tracking … complex modelling of the data using innovative methods. Knowledge of Machine Learning: Classification (Random Forest, Decision Trees, KNN), Regression. Modelling (linear, sparse, logistic), PrincipalComponentAnalysis (PCA, PCR), clustering (K-means). Profile: Strong analytical skills, ability to generate strong insights and build convincing stories. Good communication and problem-solving skills. Good organisational skills. Excellent time management. More ❯
us to study more complex systems than has previously been possible, with relevance to a wide range of societally, environmentally and industrially important areas. These include biomolecular dynamics, biomedical analysis, energy storage, catalysis and photovoltaics.?Our user community will be from both academia and industry. About the role The HiLUX project will provide higher quality spectroscopic data at higher … data analysis. We are seeking to recruit a scientific software engineer to develop the software to maximise scientific exploitation of our data. You will develop data acquisition and data analysis software for a variety of ultrafast laser spectroscopy experiments, including: Providing data analysis software for photoemission spectroscopy, to enable on-the-fly analysis of spectra. Adapting commercial … and Raman, multidimensional and sum-frequency generation (SFG). Applying relevant and new approaches to data analysis such as global analysis, principalcomponentanalysis (PCA), lifetime density analysis and machine learning. You will work as part of a multi-disciplinary team, including physicists, chemists, materials scientists and engineers. You will interface directly with scientists More ❯
around model monitoring, performance tracking, and data drift. Strong SQL skills for data extraction, joining, and transformation. Preferred Skills : Familiarity with unsupervised learning methods such as K-means, DBSCAN, PCA, or autoencoders, and their application in credit use cases like behavioral segmentation, fraud detection, or exploratory analysis Experience working in a start-up or scale-up environment with fast More ❯
class imbalance, and evaluating model performance using AUC, KS, precision/recall, etc. Understanding of model monitoring and techniques for identifying drift Experience with unsupervised learning (e.g., K-means, PCA, autoencoders) for fraud detection or segmentation Exposure to start-up or scale-up environments Familiarity with alternative data for credit scoring (e.g., device data, psychometrics) If this role looks of More ❯
class imbalance, and evaluating model performance using AUC, KS, precision/recall, etc. Understanding of model monitoring and techniques for identifying drift Experience with unsupervised learning (e.g., K-means, PCA, autoencoders) for fraud detection or segmentation Exposure to start-up or scale-up environments Familiarity with alternative data for credit scoring (e.g., device data, psychometrics) If this role looks of More ❯
clients and internal stakeholders. An eagerness to learn on the job is also required, with the company increasingly offering advanced analytical approaches to clients such as driver and factor (PCA) analysis, customer segmentation, maxdiff, conjoint etc... Prospective candidates need to be well-rounded individuals that work well within a friendly team culture and have strong analytical and communications skills. More ❯
Experience with statistical and machine learning models, such as regression-based models (e.g., logistic regression, linear regression, negative binomial regression), tree-based models (e.g., random forests), support vector machines, PCA, clustering models, matrix factorization, deep learning, etc Experience using statistical and machine learning models to contribute to company growth efforts, impacting revenue and other key business outcomes Advanced understanding of More ❯