19089-Data Analyst-Dublin Contractor

This is an 11-month initial contract.

Purpose:

The role will report to the Engineering Programs Manager. The successful candidate be responsible for digital engineering initiatives onsite and guide efforts to ensure the reliability and maintainability of equipment, processes, utilities, facilities, controls, and safety/security systems through the generation of insightful dashboards and tools. You will measure asset performance and develop cross-functional forums to review and analysis data and drive continuous improvement initiatives. This role will be a key contributor to the cross-functional site energy team which is developing our sustainability strategy.

Responsibilities:

• Querying and processing data from primary or secondary data sources.
• Create and maintain rich interactive visualizations through data interpretation and analysis
• Integrating various reporting components from multiple data sources
• Review system(s) data to identify important metrics and relationships between them
• Filter and clean data by reviewing system(s) performance indicators
• Identify, analyze, and interpret trends or patterns in complex data sets
• Working alongside teams within the business or the management team to establish data analysis & visualization needs and building the tools to support them.
• Create documentation and train users on how to use developed reports

Requirements:

• Level 7 Qualification in Engineering or Computer Science or Statistics discipline or equivalent
• Strong analytical skills with the ability to collect, organize, analyze, and disseminate significant amounts of information with attention to detail and accuracy.
• Experience with visualization tools such as Microsoft PowerApps, TIBCO Spotfire, PowerBI
• Adept at queries, report writing, and presenting findings
• Strong communication, organizational and analytical skills
• Working with both structured and unstructured databases
• Python coding experience
• SQL and Statistical Programming
• Experience with applying machine learning techniques to classification, clustering & prediction problems