Team and wider business stakeholders to analyse data and draw conclusions and insights to:
Consolidation of data from multiple data sources.
Internal and external stakeholder
Level.
MS PowerPoint must be able to summarise data analysis to communicate key messages in an engaging
Business/Data Analyst with a strong foundation in statistical analysis, data modeling, and and business intelligence. Adept at transforming data into actionable insights to drive business decisions Business Intelligence Data Analyst Africa during time of absence.
Data Analyst (JB4551) Remote (Suitable for candidates in Gauteng only) R600 000 to R620 000 Annually travel retail sector is looking for a professional Data Analyst. The business' core focus is on providing Team and wider business stakeholders to analyse data and draw conclusions and insights to enable decision-making Minimum of 3 years' experience in a related field i.e. data visualization and analytics, working with businesses environment is desirable Consolidation of data from multiple data sources Internal and external stakeholder
and recognition to its employees seeks a Senior Data Scientist who will be accountable for designing Active Learner, Change Agility, Credit Risk Analysis, Data Science, Machine Learning, Predictive Modelling Analytical, Python Programming, R Code, SQL, Statistical Data Analysis Qualifications and Experience: Degree with Stats/Math/Applied Maths/Financial Risk Management/Data Science/Engineering/Computer Science or related R or Python proficiency. Exposure to extracting data from databases is compulsory. Understanding &
a Master Data Specialist to ensure the accuracy, consistency, and integrity of master data across various degree in computer science, Information Systems, Data Management, Business Administration, or a related with systems, with specific experience in systems data quality control and maintenance (e.g. Product lists cleaning and validating large sets of data Understanding of data governance and quality principles Solid Solid understanding of fundamental data concepts The Reference Number for this positio n is NG59243 which
innovative financial services have an opportunity for a Data Engineer who will take responsibility for driving ETL systems for a big data warehouse to implement robust and trustworthy data to support high performing algorithms, predictive models and support real-time data visualisation requirements across the organisation Responsibilities: Systematic solution design of the ETL and data pipeline in line with business user specifications to the approved solution design Ensure data governance and data quality assurance standards are upheld
and IT Methodology processes is recruiting for a Data Scientist – AI Platform to offer a deep insight
as possible ROLE: Data Engineers are responsible for building and maintaining Big Data Pipelines using using GROUP Data Platforms. Data Engineers are custodians of data and must ensure that data is shared in line Boto3 ETL Docker Linux / Unix Big Data Powershell / Bash GROUP Cloud Data Hub (CDH) GROUP CDEC Blueprint Business Intelligence (BI) Experience Technical data modelling and schema design (“not drag and drop”) Boto3 ETL Docker Linux / Unix Big Data Powershell / Bash GROUP Cloud Data Hub (CDH) GROUP CDEC Blueprint
developing, and maintaining master data management solutions to ensure data integrity, consistency, and accuracy role in implementing MDM solutions and supporting data governance initiatives. MDM Solution Development Development Design, develop, and customize master data management solutions using MDM platforms such as Informatica Implement data models, hierarchies, and relationships to support business requirements and data governance workflows, business rules, and data validation processes within the MDM platform Data Integration and Interoperability
brands and retailers have a vacancy for a Senior Data Analyst whose focus will be on building out our market through powerful data pipelines. Responsibilities: Help streamline our data science and analytics analytics workflows by improving data delivery and quality to internal and external stakeholders. Use agile software Work closely with the data science and business intelligence teams to develop data models and pipelines and machine learning Model front-end and back-end data sources to help draw a more comprehensive picture