Data Analyst

Description:

• Data Cleaning and Preparation: This involves filtering the data, handling missing values, and preparing the dataset for analysis to ensure accuracy and relevance.
• Data Exploration and Analysis: Analysts use statistical tools and techniques to explore and analyze data, identifying patterns, relationships, and trends.
• Data Visualization: They create visual representations of data findings through charts, graphs, and dashboards to make the data understandable at a glance.
• Reporting: Data analysts prepare reports and presentations to communicate the insights and findings from the data to stakeholders, which can influence policy and decision-making processes.
• Collaboration: They often work with other departments to understand their data needs and help them make informed decisions based on data insights.
• Gather data from primary and secondary sources, ensuring the upkeep of databases and data systems.
• Detect, examine, and decode trends or patterns within intricate datasets.
• Coordinate with management to align business and informational priorities.
• Identify opportunities for process enhancements.
• Employ statistical techniques to scrutinize data and produce actionable business insights.
• Develop data dashboards, charts, and visual aids to support decision-making for different Products
• Convey insights through both reports and visual presentations.
• Engage with managers from various Products to specify data requirements for analysis projects tailored to their unique business processes.
• Providing advice and decision support in every aspect of the process landscape his/her role covers.
• Analyse new data analytics requirements and translate them into technical requirements, project plans and milestones.
• Identify automation, simplification, standardization, and improvement opportunities across the process.
• Collaborate with then Data Analytics network within Product and larger MAERSK organisations that harnesses subject matter expert knowledge of our data products and ensure best practices.
• Propose and implement continuous improvement initiatives within the business analysis process, including process mining, automation, and data science.
• Goals development, tracking and management.