influencing
KPAs: Analytical Modeling: Translate business requirements into suitable analytical models, extracting trends to address short-term insurance metrics. Present model functionality to relevant stakeholders upon request oversee user-acceptance testing of Data Visualisation models. Administer the business intelligence management analytics reports, extracting insights from BI models for stakeholders. Stakeholder Engagement: Build
cases. Create algorithms and build machine learning models to enhance product offerings and solve business systems to track model performance. Presentation of data science opportunities and model outcomes to a variety (must). Experience or familiarity with data science model operationalization on-prem or in the cloud (GCP and Skills Strong data exploration, analytical, modeling, and reporting skills Strong communication and
BI (Including, but not limited to Ingestion, Modelling, Transformations and Cleansing, and DAX Expressions) and predictions. - Aid in creating internal audit models and reports to identify risks and risk areas - Documentation up to date and the maintenance of semantic model R 1000 - R 1001 - Annually
develop data models and pipelines for research, reporting, and machine learning
understanding of dimensional models.
technologies including Generative AI, Large Language Models (LLMs), Natural Language Processing (NLP), Text-to-Speech systems Practical experience with Large Language Models (LLMs) Proficient in API integration and development Text-to-Speech (TTS), Speech-to-Text (STT), Large Language Models (LLMs), and integrating APIs Apply now For more
algorithms and statistical models. Build and operationalize predictive models to discover hidden insights
business insights. Ad hoc analysis (scenario modelling, impact analysis etc). Develop, test, and implement detail and accuracy. Experience in building report models with visualisation tools (Power-BI advantageous)
Data consumers transform data to a common data model for reporting and data analysis, and to provide Bricks) Data Modelling and Schema Build: In collaboration with Data Modellers, create data models and database Collaborate with Data Analysts, Software Engineers, Data Modelers, Data Scientistsm Scrum Masers and Data Warehouse Programming (Python, Java, SQL) Data Analysis and Data Modelling Data Pipelines and ETL tools (Ab Initio, ADB, Data consumers transform data to a common data model for reporting and data analysis, and to provide