seasoned Senior Business Analyst. Hybrid working model followed 2 days at office 3 days remotely per week seasoned Senior Business Analyst. Hybrid working model followed 2 days at office 3 days remotely per week that must be considered. Employ a variety of modelling techniques to analyze, confirm and communicate diagram, use cases, solution context, domain and data models, business process diagrams, activity diagrams, Product Requirements Specification (PRS) Analysis models Solution Proposal Technical Rule Configuration
managing the Data Architecture landscape.
Data modelling and to assist when working with complex Data and
architecture, Data Warehousing, Data Integration, Data Modelling, Data Governance, Master Data Management, SAP
structured and unstructured data sets. Responsibilities: Model complex business problems, discovering insights intelligent automation and predictive modelling. Build machine learning models from and utilises distributed Kafka. Provide input into Data management and modelling infrastructure requirements and adhere to the Ensure business integration through integrating model outputs into end-point production systems, are understood Measure proficiency in using the diagramming and modelling techniques vital for requirements analyses. Qualifications
vulnerabilities. Develop and maintain fraud detection models that are both accurate and minimize disruptions Optimizing Detection Models: Continuously review, analyze, and improve core models used to detect fraud legitimate transactions. This involves adjusting models and thresholds based on factors like desired alert Integrate new data sources when needed to improve model effectiveness. Assist data engineering efforts to data analysis. Experience applying statistical modelling techniques to solve complex problems. Familiarity
vulnerabilities. Develop and maintain fraud detection models that are both accurate and minimize disruptions Optimizing Detection Models: Continuously review, analyze, and improve core models used to detect fraud legitimate transactions. This involves adjusting models and thresholds based on factors like desired alert Integrate new data sources when needed to improve model effectiveness. Assist data engineering efforts to data analysis. Experience applying statistical modelling techniques to solve complex problems. Familiarity
Development Life Cycle models (Waterfall, Rapid Application Development, Spiral Model, Agile, etc.) required
TypeScript, etc.
Comfortable with data modelling, Qlik NPrinting, data mapping, back and front-end
existing Qlikview models.
Data Clean up from source.
Developing new Qlikview models.
Maintaining
CSS, C#, TypeScript, etc. Comfortable with data modelling, Qlik NPrinting, data mapping, back and front-end existing Qlikview models. Data Clean up from source. Developing new Qlikview models. Maintaining automation
statistical models and algorithms for actuarial assessments - Use of predictive modelling techniques to healthcare analytics. - Implement machine learning models to improve risk assessment and decision support healthcare and actuarial space, AI, and predictive modelling. - Contribute to research and development projects plus. - A keen interest in AI and predictive modelling, with the ability to apply these technologies
statistical models and algorithms for actuarial assessments - Use of predictive modelling techniques to healthcare analytics. - Implement machine learning models to improve risk assessment and decision support healthcare and actuarial space, AI, and predictive modelling. - Contribute to research and development projects plus. - A keen interest in AI and predictive modelling, with the ability to apply these technologies