The Data Scientist works under the guidance of the Technology team and will be responsible for analyzing raw information (particularly unstructured text data) to find patterns, build models to address business problems, and present information using various data visualization techniques and platforms. Applying and analytical approaches to solve business problems, turning the company's volumes of big data into applicable, undertaking data mining and developing and documenting robust and scalable data extraction tools
opportunity for a Data Analyst / Data Scientist in a Sage X3 ERP environment. Data gathering, cleansing cleansing, analysis, presentations Data interpretation Data Visualisation Descision support Report writing charge organisation with valuable insights into data resources Min Qualifications – Degree in Maths /
agency with a passion and knowledge in SEO, data and analytics, content and paid media. As specialists in clients grow organically online through the use of data driven strategies and insights.They are not a creative markets, which in turn allows them to use real time data to build strategies that will help clients' businesses is looking for a skilled and experienced Data Scientist / Data Engineer to join their dynamic team. The will have a strong background in data science, data analysis, or data engineering, with extensive experience
Prepares, transforms, models data and resolves conflicting sources of data and anomalies • Supports the the delivery of BI and analytical products to business users via participating in Agile development pod Agile ceremonies • Implement methods to improve data reliability and quality • Combines raw information formats • Develop and test architectures that enable data extraction and transformation for predictive or Cooperate with the Business Analyst, Data Architect and Data Visualisation Developer throughout these
1. Data Analysis: Analysing and interpreting complex data using statistical methods and tools to uncover insights and trends. 2. Data Extraction: Using ETL / ELT methods and tools like Azure Data Factory and Synapse Synapse. 3. Data Visualization: Creating visualizations and dashboards to communicate data insights effectively 4. Data Cleaning: Cleaning and preprocessing data to ensure accuracy and completeness. 5. Data Modelling: Working collaboratively with other members of the data science team and other departments to identify business
processing data from over 200 source systems. The role involves cleaning and aggregating data through our will improve and streamline processes regarding data flow and quality, working both independently and the possibility of permanency
Qualifications
Prepares, transforms, models data and resolves conflicting sources of data and anomalies Supports the delivery delivery of BI and analytical products to business users via participating in Agile development pod activities including Agile ceremonies Implement methods to improve data reliability and quality Combines raw information formats Develop and test architectures that enable data extraction and transformation for predictive or Cooperate with the Business Analyst, Data Architect and Data Visualisation Developer throughout these
The Administrator/Data Capturer is responsible for assisting the Project Manager with administration Project Manager with administration documentation Data Catpturing Collecting invoices Initial ranking of
Join Our Client's Team as a CRM Commercial Data Analyst Our client, a leading global hygiene and health company, is excited to welcome a CRM Commercial Data Analyst to their team in Johannesburg. In this role cross-functional global team Contribute to the development of a data-driven performance mindset/way-of-working across Business Unit by maximizing leverage of commercial analytics. Development of sales services tools and provide reports and dashboards in alignment with Customer Analytics best practices. Requirements: Degree preferably
is in need of a Data Analyst who will be responsible for actively identifying any data conflicts and patterns ensure we have data accuracy, integrity and the best possible datasets for our reports. Data Gathering: Collect Collect data from various sources, ensuring accuracy and completeness. Data Cleaning: Pre-process and and clean data to prepare it for analysis, which might involve handling missing values, outliers, and inconsistencies inconsistencies. Data Analysis: Utilise SQL to query databases and extract relevant information for analysis