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
structures and develops long term sustainable data models. Collaborate with business stakeholders to understand effective BI solutions. Design and develop data models to support reporting and analytics. Create and and maintain logical and physical data models for BI applications. Develop Extract, Transform, Load (ETL) confidentiality. Document BI solutions, including data models, ETL processes, and report specifications. Create Analysis Services), Power Pivot. Strong Dimension Modeling experience. Experience in handling heterogeneous
deploying BI solutions (e.g., data warehousing models) and creating visualizations and reports for requested in SQL, SSRS, Excel Work with the team in data modelling, database design, creating efficient SQL for fast Production experience working with at least modern modelling tools - on premise and / or in the cloud (e.g Analytics) Production experience working with DAX modelling measures (e.g. Power BI, Analysis Services, DAX
deploying BI solutions (e.g., data warehousing models) and creating visualizations and reports for requested SSRS, Excel
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
(Informatics) - Essential
business objectives.
Qualifications and Experi
data analysis, machine learning, and predictive modelling.
requirements, and through a structured process, modelling, validating, and translating it into business using alternative views such as flows, diagrams, models, and use cases when applicable. Business Process
and Optimisation. Database Management Create Data Models in a Structured data format to enable analysis components of the data tables, data queries and data models. Identify valuable data sources and automate collection