Under the supervision of the Head, the Data Management Scientist will play a critical role in the efforts of Institutional Reporting and Data Governance (IRDG) in the Office of the President (OP) to build data integration and warehousing solutions to support analytical applications for institutional-wide reporting, data governance, and ultimately data informed executive decision-making at KAUST.
Data Repositories and Data Marts:
- Automate the compilation, merging, cleaning, manipulation, organization, and maintenance of data
- Write well documented (and backed up) programs/scripts/queries
- Create documentation (and/or training materials)
- Independently review, verify, reconcile, and update databases
- Conduct data validation, diagnostics, and cleaning exercises and audits
Implementation of Machine Learning:
- Develop automated internet and internal ERP crawling platforms
- Automate the capturing, consolidation, and extraction of relevant structured and unstructured data/information
- Documentation on the structure of databases, and the format which elements are maintained in those databases
- Create codebooks that describe contents of databases and descriptions of variables and codes (and transformations) within
- Produce mappings/cross-walks of various databases and/or tables
- Automation of customized and/or extended tabular, graphical, and/or analytical reports
- Website development, applications, and analytics
- Provide technical training to facilitate organizational-wide informational needs
- Work closely with IT on technical issues surrounding: accounts, licenses, software acquisition/installation/updates, website/web applications, servers, databases, etc.
- Advanced Excel proficiencies
- Experience with collaboration and web content management applications (i.e., SharePoint, Google Docs, SmartSheet, etc.)
- Demonstrable experience with web forms and survey administration tools (i.e., Wufoo, FormStack, GoogleForms, SurveyMonkey, Qualtrics, etc.)
- Familiarity with storage and processing of big data (i.e., Hadoop, Cloud Dataflow, Big Query, Google Mesa, etc.)
- Familiarity with implementation and maintenance of ETL methodologies and solutions, business objects, query, and reporting functionalities in ERP systems (i.e., SAP, Hyperion, Oracle, Banner, PeopleSoft, etc.)
- Strong and demonstrable skills in the management and manipulation of large and complex relational databases (i.e., SAP HANA, SQL Server, Access, etc.)
- Technical expertise with database administration and various development, programming, and writing complex queries (SQL, NoSQL, .NET, VB, C/C++, Python, etc.)
- Experience with Unix/Linux operating systems, shells, and tools such as grep, find, wget, awk, etc.
- Understanding of, and commitment to, KAUST’s vision, mission, and values
- Familiarity with KAUST databases and systems and deep understanding of linkages and dependencies across institutional student, faculty, research, financial, HR, and other data
- Demonstrable SAP business intelligence reporting tools and information builder experience (i.e., SAP Crystal Reports, SAP Business Explorer, SAP Lumira, etc.)
- Programming experience in SAP HANA XSA
- Experience designing, installing, and configuring new/existing Unix/Linux systems; Unix/Linux environment troubleshooting, remediation, etc.
- Experience in building self-updating real-time front end components using HTTP/REST API calls and the Websocket protocol
- Experience with statistical analysis software (i.e., Stata, R, SAS, etc.)
- Experience with mathematical open source packages (i.e., NumPy, SciPy, Pandas, etc.)
Bachelor’s degree in an analytical field (i.e., Computer Science, Engineering, Mathematics, Business Analytics, Operations Research, Economics, Quantitative Social Science)
Master’s or PhD degree in an analytical field (i.e., Computer Science, Engineering, Mathematics, Business Analytics, Operations Research, Economics, Quantitative Social Science) from an accredited college or university
At least five (7) years of related post-baccalaureate professional experience
Experience in a formal organizational setting focused on ERP systems, data warehouses, database management, programming, automation, machine learning, APIs, visualization, web applications, analytics
Experience in a university environment and/or setting
Experience in an institutional research, planning, information management, analytics, and/or similar office/function
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